This document summarizes a research paper that proposes a machine learning approach to identify disease-treatment relationships from biomedical text. It extracts sentences mentioning diseases and treatments from medical publications and classifies the semantic relationships between them. The researchers evaluate their methodology on a dataset of sentences annotated with diseases, treatments and their relationships. Their results show the machine learning models can reliably extract this information and outperform previous methods on the same data. The proposed approach could be integrated into applications to disseminate healthcare information from published literature to medical professionals and patients.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
This document proposes a new convolutional neural network based multimodal disease risk prediction algorithm (CNN-MDRP) that uses both structured and unstructured data from healthcare to improve disease prediction accuracy. It aims to address challenges from incomplete medical data and regional differences in diseases. The algorithm reconstructs missing data, identifies major regional chronic diseases, extracts useful features from structured and unstructured text data using CNN, and combines these for risk prediction. Experimental results show the CNN-MDRP approach achieves 94.8% accuracy, faster than existing CNN-based methods that only use single data types.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
The document summarizes a disease prediction system for rural health services presented by two students. The key points are:
1. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms.
2. It seeks to enhance access to medical specialists for rural communities and improve quality of healthcare.
3. The expected outcomes are conducting experiments to evaluate the performance of using 7 machine learning algorithms to predict diseases from symptoms and having doctors select the correct diagnosis from the predictions.
The state of the art in behavioral machine learning for healthcareAfrica Perianez
The use of smart devices and wearables is becoming increasingly popular. This allows patients to be continuously monitored and provides a huge amount of health-related data that, if properly analyzed, can be used to improve their health by predicting potential future conditions. Advanced machine learning techniques do permit such analysis, and thus serve to forecast the evolution and health challenges of individual patients. This includes, for instance, issues as critical as early detection of heart disease.
But, moreover, the whole healthcare sector is currently undergoing a profound transformation. The rich profusion of digital data is fostering a move from more traditional approaches towards a data-driven prevention model.
In this talk I survey state of the art methods that allowed an AI-based early diagnosis and risk assessment for individual patients, using information that may include health records, genomic and wearable device data, medical imagery and online physician reviews. I will focus on methods that can be employed to forecast future events affecting a specific patient and serve to evaluate wearable device data and assist healthcare industry in undertaking a patient-focused data-driven preventive approach. Additionally, I introduce how machine-learning-based gamification techniques can be employed to motivate individual users to improve their health condition and achieve personalized challenges.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
Heart Disease Identification Method Using Machine Learnin in E-healthcare.SUJIT SHIBAPRASAD MAITY
This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Block diagrams and flow charts illustrate the data preprocessing, model training, and web application development steps to classify patients as having heart disease or not and evaluate model performance. The developed system achieves high accuracy for heart disease prediction.
Existing model uses structured data to predict the patients of either high risk or low risk.
But for a complex disease, structured data is not a good way to describe the disease.
We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
In this paper, we mainly focus on the risk prediction of cerebral infarction.
DISEASE PREDICTION BY MACHINE LEARNING OVER BIG DATA FROM HEALTHCARE COMMUNI...Nexgen Technology
This document proposes a new convolutional neural network based multimodal disease risk prediction algorithm (CNN-MDRP) that uses both structured and unstructured data from healthcare to improve disease prediction accuracy. It aims to address challenges from incomplete medical data and regional differences in diseases. The algorithm reconstructs missing data, identifies major regional chronic diseases, extracts useful features from structured and unstructured text data using CNN, and combines these for risk prediction. Experimental results show the CNN-MDRP approach achieves 94.8% accuracy, faster than existing CNN-based methods that only use single data types.
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
The document summarizes a disease prediction system for rural health services presented by two students. The key points are:
1. The system aims to provide quick medical diagnosis to rural patients using machine learning algorithms like SVM, RF, DT, NB, ANN, KNN, and LR to recognize diseases from symptoms.
2. It seeks to enhance access to medical specialists for rural communities and improve quality of healthcare.
3. The expected outcomes are conducting experiments to evaluate the performance of using 7 machine learning algorithms to predict diseases from symptoms and having doctors select the correct diagnosis from the predictions.
The state of the art in behavioral machine learning for healthcareAfrica Perianez
The use of smart devices and wearables is becoming increasingly popular. This allows patients to be continuously monitored and provides a huge amount of health-related data that, if properly analyzed, can be used to improve their health by predicting potential future conditions. Advanced machine learning techniques do permit such analysis, and thus serve to forecast the evolution and health challenges of individual patients. This includes, for instance, issues as critical as early detection of heart disease.
But, moreover, the whole healthcare sector is currently undergoing a profound transformation. The rich profusion of digital data is fostering a move from more traditional approaches towards a data-driven prevention model.
In this talk I survey state of the art methods that allowed an AI-based early diagnosis and risk assessment for individual patients, using information that may include health records, genomic and wearable device data, medical imagery and online physician reviews. I will focus on methods that can be employed to forecast future events affecting a specific patient and serve to evaluate wearable device data and assist healthcare industry in undertaking a patient-focused data-driven preventive approach. Additionally, I introduce how machine-learning-based gamification techniques can be employed to motivate individual users to improve their health condition and achieve personalized challenges.
Machine Learning for Disease PredictionMustafa Oğuz
A great application field of machine learning is predicting diseases. This presentation introduces what is preventable diseases and deaths. Then examines three diverse papers to explain what has been done in the field and how the technology works. Finishes with future possibilities and enablers of the disease prediction technology.
following topics are discussed inside the PPT:
Introduction
Objective
Motivation
Literature Survey
Some Key Features of Disease
Plan of Action
Methodology Adopted
Data Collection
Steps to be Performed
Functional Architecture
Heart Disease Identification Method Using Machine Learnin in E-healthcare.SUJIT SHIBAPRASAD MAITY
This document describes a student project that aims to develop a machine learning model for heart disease identification and prediction. It discusses existing heart disease diagnosis techniques, identifies the problem and requirements, outlines the proposed algorithm and methodology using supervised learning classification algorithms like K-Nearest Neighbors and logistic regression. Block diagrams and flow charts illustrate the data preprocessing, model training, and web application development steps to classify patients as having heart disease or not and evaluate model performance. The developed system achieves high accuracy for heart disease prediction.
Disease prediction and doctor recommendation systemsabafarheen
This document presents a disease prediction and doctor recommendation system. The system aims to analyze healthcare data to predict diseases and provide accurate risk predictions to help control disease. It uses a naive Bayes classification algorithm to predict diseases based on patient symptoms and medical profiles. The system then recommends specialist doctors for the predicted disease based on filters like location, cost, experience level, and reviews. It was found to accurately predict diseases 63-83% of the time based on previous studies. The goal is to improve prediction using more symptoms and medical cases.
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document summarizes research on using data mining techniques to predict heart disease. It discusses previous work using classification, clustering, association rule mining and other techniques on several heart disease datasets. Classification algorithms like naive bayes, decision trees and neural networks have been widely used with naive bayes found to often provide the best performance. Feature selection and attribute reduction are also examined. The document provides an overview of the key steps and techniques in medical data mining and predictive analysis for heart disease.
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes using fuzzy logic and data mining techniques to predict medical diseases from patient data. The paper extracts meaningful patterns of diseases from clinical guidelines using text mining. It then applies fuzzy logic to patient data through the clinical guidelines to determine the possibility of different diseases. Prior work on applying data mining to medical data is also reviewed, highlighting challenges like a lack of standardized clinical vocabulary and issues with data cleaning. The proposed approach uses fuzzy rules sets and association mining on clinical guidelines to predict medical diseases.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict diseases based on patient health data. Specifically, it proposes using a k-means machine learning algorithm to analyze structured and unstructured patient data stored in a healthcare dataset. This would allow the system to predict diseases and outbreaks with greater accuracy than existing methods. The k-means algorithm is applied to cluster patient data, including symptoms from sensors and medical records, to identify patterns and deliver predictive results. The goal is to enable early disease prediction and prevention through analysis of big healthcare data using machine learning.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
This document presents a comparative study of various data mining techniques for predicting heart disease using collected and standard heart disease datasets. Five classifiers - KStar, J48, SMO, Bayes Net, and MLP - were evaluated based on their accuracy and training time. SMO had the highest accuracy of 84-89% and MLP had the lowest training time of 0.33-0.75 seconds. The techniques are also compared based on their average classification variance. The study concludes with receiver operating characteristic curves showing the performance of the techniques on the two datasets.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict diseases based on patient symptoms. Specifically, it proposes using naive bayes, k-nearest neighbors (KNN), and logistic regression algorithms on structured and unstructured hospital data to predict diseases like diabetes, malaria, jaundice, dengue, and tuberculosis. The system is intended to make disease prediction more accessible to end users by analyzing their symptoms without needing to visit a doctor. It aims to improve prediction accuracy by handling both structured and unstructured data using machine learning models.
This presentation discusses analyzing and predicting heart disease using machine learning techniques. The main goal is to build a model to predict heart disease occurrence based on risk factors. Several classification algorithms will be tested, including K-nearest neighbors, decision trees, logistic regression, naive Bayes, support vector machines, and random forests. Publicly available datasets with over 400 cases will be used to train and evaluate the models. Existing literature has used techniques like association rules, neural networks, and clustering for heart disease prediction, achieving accuracy between 70-90%. The proposed approach aims to improve prediction performance.
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
Android Based Questionnaires Application for Heart Disease Prediction Systemijtsrd
Today classification techniques in data mining are most popular to prediction and data exploration. This Heart Disease Prediction System HDPS is using Naive Bayesian Classification with a comparison for simple probability and that of Jelinek Mercer JM Smoothing. It is implemented as an Android based application user must be feedback and answers the questions then can be seen the result as user desired in different ways exactly heart disease is present or not and then with predictions No, Low, Average, High, Very High . And the system will be provided required suggestions such as doctor details and medications to patients could be able. It will be also proved that enhanced Naive Bayes with Jelinek Mercer smoothing technique is also effective to eliminate the noise for prediction the heart disease. This system can also calculate classifier accuracy by using precision and recall. Nan Yu Hlaing | Phyu Pyar Moe "Android Based Questionnaires Application for Heart Disease Prediction System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26750.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26750/android-based-questionnaires-application-for-heart-disease-prediction-system/nan-yu-hlaing
This document discusses using biologically inspired machine learning techniques to categorize tumor types. It proposes applying genetic search, particle swarm optimization, and evolutionary search algorithms to a dataset with 18 attributes and 339 tumor instances to eliminate irrelevant features before classification. The results are evaluated using performance metrics and show biologically inspired models like multi-layer perceptron with optimization techniques can help medical experts efficiently predict and diagnose tumors.
A data mining approach for prediction of heart disease using neural networksIAEME Publication
This document describes a study that developed a heart disease prediction system using neural networks and data mining techniques. [1] It used a multilayer perceptron neural network with backpropagation algorithm to predict heart disease based on 13 medical parameters from patient data. [2] To improve accuracy, it added two more parameters - obesity and smoking. [3] The system was able to predict heart disease with nearly 100% accuracy based on testing 270 patient records.
This describes the techniques that are used for prediction of heart diseases using the concept of data mining.It states about IHPDS(Intelligent Heart Disease Prediction System)
Psdot 14 using data mining techniques in heartZTech Proje
The document proposes applying data mining techniques to identify suitable heart disease treatments. It discusses using single and hybrid data mining on diagnosis and treatment data to determine if models can reliably predict treatments as they do diagnoses. The proposed system would apply various data mining algorithms to both diagnosis and treatment data to investigate if hybrid models improve treatment prediction accuracy over single techniques.
A Heart Disease Prediction Model using Logistic Regressionijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Manoj | G. Suguna Mani"A Heart Disease Prediction Model using Logistic Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11401.pdf http://www.ijtsrd.com/computer-science/data-miining/11401/a-heart-disease-prediction-model-using-logistic-regression/k-sandhya-rani
LinuxCon Brazil 2011 - Hack your team, your Department, and Your Organization...John Mertic
Many of us complain about how ""pointy hair bosses"", ridiculous IT policies, and a culture of complacency make it difficult if not impossible to function successfully as a developer. But how can the person on the low end of the organizational hierarchy make a difference for the better ( without being fired ) ? In this talk, I'll give you some ideas of what I've done in the past to help be a positive agent of change in organizations I've been a part of, and what lessons I've learned from it. We'll look at implementing development tools incognito, engaging with higher-ups, and dealing with the failures that go along the way.
Organise yourself! An introduction to web toolsJo Alcock
The document introduces various web tools that can help with time management, research, collaboration, and keeping up-to-date. It recommends tools like calendars, to-do lists, RSS readers, social bookmarking services, and online document storage and creation services to organize information and work online. These tools can be accessed from any computer and require no specialist software, making them convenient to use. Examples of specific tools mentioned include Google Calendar, Remember the Milk, del.icio.us, Facebook, Google Docs, and SurveyMonkey.
Disease prediction and doctor recommendation systemsabafarheen
This document presents a disease prediction and doctor recommendation system. The system aims to analyze healthcare data to predict diseases and provide accurate risk predictions to help control disease. It uses a naive Bayes classification algorithm to predict diseases based on patient symptoms and medical profiles. The system then recommends specialist doctors for the predicted disease based on filters like location, cost, experience level, and reviews. It was found to accurately predict diseases 63-83% of the time based on previous studies. The goal is to improve prediction using more symptoms and medical cases.
The document proposes a heart attack prediction system using fuzzy C-means clustering. The system takes in a patient's medical attributes like age, blood pressure, and artery thickness from their records. It then uses a fuzzy C-means algorithm to cluster this data and predict the patient's risk of a heart attack. The system is intended to help doctors make earlier diagnoses compared to only relying on their experience and a patient's records.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document summarizes research on using data mining techniques to predict heart disease. It discusses previous work using classification, clustering, association rule mining and other techniques on several heart disease datasets. Classification algorithms like naive bayes, decision trees and neural networks have been widely used with naive bayes found to often provide the best performance. Feature selection and attribute reduction are also examined. The document provides an overview of the key steps and techniques in medical data mining and predictive analysis for heart disease.
Prediction of Heart Disease using Machine Learning Algorithms: A Surveyrahulmonikasharma
According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches’ working in this field.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes a research paper that proposes using fuzzy logic and data mining techniques to predict medical diseases from patient data. The paper extracts meaningful patterns of diseases from clinical guidelines using text mining. It then applies fuzzy logic to patient data through the clinical guidelines to determine the possibility of different diseases. Prior work on applying data mining to medical data is also reviewed, highlighting challenges like a lack of standardized clinical vocabulary and issues with data cleaning. The proposed approach uses fuzzy rules sets and association mining on clinical guidelines to predict medical diseases.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to predict diseases based on patient health data. Specifically, it proposes using a k-means machine learning algorithm to analyze structured and unstructured patient data stored in a healthcare dataset. This would allow the system to predict diseases and outbreaks with greater accuracy than existing methods. The k-means algorithm is applied to cluster patient data, including symptoms from sensors and medical records, to identify patterns and deliver predictive results. The goal is to enable early disease prediction and prevention through analysis of big healthcare data using machine learning.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
This document presents a comparative study of various data mining techniques for predicting heart disease using collected and standard heart disease datasets. Five classifiers - KStar, J48, SMO, Bayes Net, and MLP - were evaluated based on their accuracy and training time. SMO had the highest accuracy of 84-89% and MLP had the lowest training time of 0.33-0.75 seconds. The techniques are also compared based on their average classification variance. The study concludes with receiver operating characteristic curves showing the performance of the techniques on the two datasets.
We are predicting Heart Disease by Taking 14 Medical Parameters as an inputs through 2 data Minning Techniques(Decision Tree(Faster) And KNN neighbour Algorithms(Slower)).
And Visualizing The dataset.If the output 1 then it means Higher Chances of getting Heart Attack ,if 0 then it means Less chances of Heart Attack.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict diseases based on patient symptoms. Specifically, it proposes using naive bayes, k-nearest neighbors (KNN), and logistic regression algorithms on structured and unstructured hospital data to predict diseases like diabetes, malaria, jaundice, dengue, and tuberculosis. The system is intended to make disease prediction more accessible to end users by analyzing their symptoms without needing to visit a doctor. It aims to improve prediction accuracy by handling both structured and unstructured data using machine learning models.
This presentation discusses analyzing and predicting heart disease using machine learning techniques. The main goal is to build a model to predict heart disease occurrence based on risk factors. Several classification algorithms will be tested, including K-nearest neighbors, decision trees, logistic regression, naive Bayes, support vector machines, and random forests. Publicly available datasets with over 400 cases will be used to train and evaluate the models. Existing literature has used techniques like association rules, neural networks, and clustering for heart disease prediction, achieving accuracy between 70-90%. The proposed approach aims to improve prediction performance.
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
Android Based Questionnaires Application for Heart Disease Prediction Systemijtsrd
Today classification techniques in data mining are most popular to prediction and data exploration. This Heart Disease Prediction System HDPS is using Naive Bayesian Classification with a comparison for simple probability and that of Jelinek Mercer JM Smoothing. It is implemented as an Android based application user must be feedback and answers the questions then can be seen the result as user desired in different ways exactly heart disease is present or not and then with predictions No, Low, Average, High, Very High . And the system will be provided required suggestions such as doctor details and medications to patients could be able. It will be also proved that enhanced Naive Bayes with Jelinek Mercer smoothing technique is also effective to eliminate the noise for prediction the heart disease. This system can also calculate classifier accuracy by using precision and recall. Nan Yu Hlaing | Phyu Pyar Moe "Android Based Questionnaires Application for Heart Disease Prediction System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26750.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26750/android-based-questionnaires-application-for-heart-disease-prediction-system/nan-yu-hlaing
This document discusses using biologically inspired machine learning techniques to categorize tumor types. It proposes applying genetic search, particle swarm optimization, and evolutionary search algorithms to a dataset with 18 attributes and 339 tumor instances to eliminate irrelevant features before classification. The results are evaluated using performance metrics and show biologically inspired models like multi-layer perceptron with optimization techniques can help medical experts efficiently predict and diagnose tumors.
A data mining approach for prediction of heart disease using neural networksIAEME Publication
This document describes a study that developed a heart disease prediction system using neural networks and data mining techniques. [1] It used a multilayer perceptron neural network with backpropagation algorithm to predict heart disease based on 13 medical parameters from patient data. [2] To improve accuracy, it added two more parameters - obesity and smoking. [3] The system was able to predict heart disease with nearly 100% accuracy based on testing 270 patient records.
This describes the techniques that are used for prediction of heart diseases using the concept of data mining.It states about IHPDS(Intelligent Heart Disease Prediction System)
Psdot 14 using data mining techniques in heartZTech Proje
The document proposes applying data mining techniques to identify suitable heart disease treatments. It discusses using single and hybrid data mining on diagnosis and treatment data to determine if models can reliably predict treatments as they do diagnoses. The proposed system would apply various data mining algorithms to both diagnosis and treatment data to investigate if hybrid models improve treatment prediction accuracy over single techniques.
A Heart Disease Prediction Model using Logistic Regressionijtsrd
The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease. K. Sandhya Rani | M. Sai Manoj | G. Suguna Mani"A Heart Disease Prediction Model using Logistic Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11401.pdf http://www.ijtsrd.com/computer-science/data-miining/11401/a-heart-disease-prediction-model-using-logistic-regression/k-sandhya-rani
LinuxCon Brazil 2011 - Hack your team, your Department, and Your Organization...John Mertic
Many of us complain about how ""pointy hair bosses"", ridiculous IT policies, and a culture of complacency make it difficult if not impossible to function successfully as a developer. But how can the person on the low end of the organizational hierarchy make a difference for the better ( without being fired ) ? In this talk, I'll give you some ideas of what I've done in the past to help be a positive agent of change in organizations I've been a part of, and what lessons I've learned from it. We'll look at implementing development tools incognito, engaging with higher-ups, and dealing with the failures that go along the way.
Organise yourself! An introduction to web toolsJo Alcock
The document introduces various web tools that can help with time management, research, collaboration, and keeping up-to-date. It recommends tools like calendars, to-do lists, RSS readers, social bookmarking services, and online document storage and creation services to organize information and work online. These tools can be accessed from any computer and require no specialist software, making them convenient to use. Examples of specific tools mentioned include Google Calendar, Remember the Milk, del.icio.us, Facebook, Google Docs, and SurveyMonkey.
This document summarizes and lists various online productivity tools related to Getting Things Done (GTD) methodology. It provides over 30 links to specific tools for task management, meetings, goals, calendars, reminders, project management and more. The tools listed include Remember the Milk, Voo2do, Todoist, Nozbe, OmniFocus, Backpack and ToodleDo. Additional resources for learning more about GTD and related organization tools are also referenced.
Evidence at Your Fingertips: Elegance Solutions for Painful ProcessesEric Robertson
This document summarizes a presentation on leveraging internet technologies and web 2.0 tools to address barriers to evidence-based practice. It discusses how RSS feeds, aggregators, advanced searching, blogging, wikis and other collaborative web tools can help practitioners efficiently gather and share information to incorporate evidence into practice. Examples are provided for how different users like clinicians, students and clinic managers could apply these technologies.
The document describes the DACUM (Developing A Curriculum) process for analyzing jobs. It discusses orienting the committee members, reviewing the occupation, identifying duties and tasks, refining statements, and required knowledge. Key aspects of DACUM include having expert workers define the job, describing tasks with a verb-object format, and refining statements through group discussion.
Stop Wasting Time: Ten Things You Can Do to Make Yourself More EfficientScott Abel
Presented by Scott Abel, The Content Wrangler Community, Tuesday, June 3, 2008 at the Society for Technical Communication Summit, Philadelphia, PA.
Most folks complain they don't have enough time to move their careers forward. Most of the time, this is simply not true. There's plenty of time in each day, but we're used to wasting it on time-sucking tasks that provide little or no business value. Learn ten of the biggest time-consuming tasks we perform each day and how to accomplish them more efficiently.
Slides from a webinar for B2BMarketing Magazine on 13/03/13. Covers:
- reviewing your approach to work
- Tips for handling email quicker
- Using collaboration platforms as an email alternative
Having reviewed this presentation, why not download my guide to Productivity Tools. Over 50 tools are reviewed covering everything from Task Managers, Note taking apps and utilities I use every day. Get it at at http://productivityguideform.purplesalix.com/productivityguide
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document summarizes a research paper that proposes using machine learning algorithms and natural language processing techniques to extract disease and treatment information from medical texts. It discusses using a pipeline of tasks including identifying relevant sentences, representing the sentences, and classifying the relationships between diseases and treatments. The paper reviews previous work on using algorithms like naive Bayes and conditional naive Bayes for information extraction and relation classification. It proposes applying machine learning and natural language processing to biomedical texts from sources like Medline to automatically extract symptoms, causes, and treatments for diseases specified in user queries. This extracted information could help doctors and patients by providing structured medical knowledge in a time-saving manner.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Ontology oriented concept based clusteringeSAT Journals
Abstract Worldwide health centre scientists, physicians and other patients are accessing, analyzing, integrating and storing massive amounts of digital medical data in different database. The potential for retrieval of information is vast and daunting. The objective of our approach is to differentiate relevant information from irrelevant through user friendly and efficient search algorithms. The traditional solution employs keyword based search without the semantic consideration. So the keyword retrieval may return inaccurate and incomplete results. In order to overcome the problem of information retrieval from this huge amount of database, there is a need for concept based clustering method in ontology. In the proposed method, WorldNet is integrated in order to match the synonyms for the identified keywords so as to obtain the accurate information and it presents the concept based clustering developed using k-means algorithm in accordance with the principles of ontology so that the importance of words of a cluster can be identified. Keywords: Ontology, Concept based clustering, K-means algorithm and information retrieval.
Nlp based retrieval of medical information for diagnosis of human diseaseseSAT Publishing House
This document describes a proposed natural language processing (NLP) system to retrieve medical information from clinical documents for disease diagnosis. The system would use NLP techniques like named entity recognition, part-of-speech tagging, and relationship extraction to process both clinical documents and user queries. For queries asking for disease information, the system would retrieve and score relevant documents, then output disease information. For queries describing symptoms, the system would attempt to output the corresponding disease name. The system would be implemented using modules for data extraction, processing, query analysis, document retrieval and scoring, and output filtering.
Nlp based retrieval of medical information for diagnosis of human diseaseseSAT Journals
Abstract NLP Based Retrieval of Medical Information is the extraction of medical data from narrative clinical documents. In this paper, we provide the way to diagnose diseases with the help of natural language interpretation and classification techniques. However extraction of medical information is difficult task due to complex symptom names and complex disease names. For diagnosis we will be using two approaches, one is getting disease names with the help of classifiers and another way is using the patterns with the help of NLP for getting the information related to diseases. These both approaches will be applied according to the question type. Keywords: NLP, narrative text, extraction, medical information, expert system
Biomedical indexing and retrieval system based on language modeling approachijseajournal
This summarizes a research paper that proposes a biomedical indexing and retrieval system called BIOINSY. It uses a language modeling approach to select the best Medical Subject Headings (MeSH) descriptors to index medical articles from sources like PUBMED. The system first preprocesses articles by splitting text, stemming words, and removing stop words. It then extracts terms using a hybrid linguistic and statistical approach. Terms are weighted based on semantic relationships in MeSH, not just statistics. Descriptors are selected by disambiguating terms and estimating the probability a descriptor was generated by the article's language model. Experiments showed the effectiveness of this conceptual indexing approach.
T OP K-O PINION D ECISIONS R ETRIEVAL IN H EALTHCARE S YSTEM csandit
The aim of this paper is to use data mining techniq
ue and opinion mining(OM) concepts to the
field of health informatics. The decision making in
health informatics involves number of
opinions given by the group of medical experts for
specific disease in the form of decision based
opinions which will be presented in medical databas
e in the form of text. These decision based
opinions are then mined from database with the help
of mining technique. Text document
clustering plays major role in the fast developing
information Explosion. It is considered as tool
for performing information based operations. Text d
ocument clustering generates clusters from
whole document collection automatically, normally K
-means clustering technique used for text
document clustering. In this paper we use Bisecting
K-means clustering technique and it is
better compared to traditional K-means technique. T
he objective is to study the revealed
groupings of similar opinion-types associated with
the likelihood of physicians and medical
experts.
Pharma Social Media Listening: Unlocking Hidden Insights | WhitepaperRNayak3
Social media listening offers valuable business insights for pharma companies, but using open-source data can be complex. Explore how topic modeling can address this issue.
Unlocking Hidden Insights for Pharma with Social Media ListeningRNayak3
Social media listening offers valuable business insights for pharma companies, but using open-source data can be complex. Explore how topic modeling can address this issue.
TWO LEVEL SELF-SUPERVISED RELATION EXTRACTION FROM MEDLINE USING UMLSIJDKP
The biomedical research literature is one among many other domains that hides a precious knowledge, and
the biomedical community made an extensive use of this scientific literature to discover the facts of
biomedical entities, such as disease, drugs,etc.MEDLINE is a huge database of biomedical research
papers which remain a significantly underutilized source of biological information. Discovering the useful
knowledge from such huge corpus leads to various problems related to the type of information such as the
concepts related to the domain of texts and the semantic relationship associated with them. In this paper,
we propose a Two-level model for Self-supervised relation extraction from MEDLINE using Unified
Medical Language System (UMLS) Knowledge base. The model uses a Self-supervised Approach for
Relation Extraction (RE) by constructing enhanced training examples using information from UMLS. The
model shows a better result in comparison with current state of the art and naïve approaches
Common Models in Health Informatics Evaluation.docxwrite31
1) The author was assigned Scenario 2, which involves evaluating the impact of a new decision support system on adverse events at a hospital. Two models that could be used are the technology acceptance model (TAM) and diffusion of innovations (DOI) model.
2) The TAM would be useful to evaluate how physicians and nurses accept and use the new system by examining perceived ease of use and perceived usefulness. The DOI model could be used to evaluate how the system spreads and is adopted within the hospital by looking at factors that influence its diffusion.
3) It is important to consider the intended goal of the evaluation, which in this case is to determine the system's impact on adverse events. The viewpoint
Common Models in Health Informatics EvaluationHave you ever watche.docxbartholomeocoombs
Common Models in Health Informatics Evaluation
Have you ever watched a movie in which the same scene was shown several times but as viewed by different individuals? Or, have you watched a detective show in which the witnesses all had differing accounts? The same can hold true for conducting an evaluation of a health information technology project. How you plan and conduct the evaluation is largely dependent on the viewpoint you assume and the perspective with which you approach the evaluation.
Consider a new patient discharge protocol at a small hospital. Do you want to know how the patient feels about the process? Do you want to gather the opinions of nurses who are using this process? Perhaps you want to determine if it is saving the hospital money by freeing up bed space in a more timely fashion. Obtaining each of these viewpoints would require a different approach. Depending on the goal of your evaluation, the model and viewpoint you opt to use will likely vary.
In this Discussion, determine which evaluation model would be most effective for evaluating the health information technology described in one of the scenarios below. Your Instructor will assign a specific scenario by Day 1 of this week.
Scenario 1:
You have recently provided a training program to help nurses and physicians become proficient in the use of a new bedside medication verification (BMV) system.
Scenario 2:
The Chief Medical Officer at your hospital is interested in finding out the impact of a new decision support system on the number of adverse events occurring in the past year.
Scenario 3:
You are helping with the design of a new outpatient surgery center to be built adjacent to the hospital. You are tasked with evaluating the opinions of physicians, nurses, and the general public toward this facility.
To prepare:
Review the information on the types of evaluation models covered in this week’s Learning Resources.
Determine which model would be most appropriate to use for evaluation in the scenario to which you were assigned.
Consider why the viewpoint of the scenario or situation would impact the model used.
View the scenario from a different viewpoint, and consider how a different model might be used.
Reflect on the importance of basing an evaluation on a model.
By tomorrow 12/13/2016 at 9pm, post a minimum of 550 words in APA format with a minimum of 3 references from the list below, which include the level one headings as numbered below:
1)
Post
which scenario (1, 2, or 3) you were assigned and two different models that could be utilized to approach the evaluation.
2)
Explain why you selected those models and how you would use them.
3)
Explain why it is important to consider the intended goal of the evaluation and the viewpoint that is selected.
4)
Finally, assess the importance of basing an evaluation on a model. Justify your response.
Required Readings
Technology Acceptance Model
Kowitlawakul, Y. (2011). The Technology Acceptance Model: Predictin.
Application Evaluation Project Part 1 Evaluation Plan FocusTec.docxalfredai53p
Application: Evaluation Project Part 1: Evaluation Plan Focus
Technology increases human effectiveness. Using a lever, you can move an object several times your size. In an airplane, you can move exponentially faster than on foot. Using the Internet, you can access information much more quickly than at a library. What possibilities like this exist in the nursing field? What health information technologies can amplify your impact as a nurse far more than ever before? In this Evaluation Project, you will have the opportunity to answer these questions.
Because of the great differences between HIT systems and different goals of an evaluation, there is no one-size-fits-all evaluation plan. Different technologies require different evaluation methods. Consequently, in this part of your Evaluation Project, you will conduct research on how system implementations similar to the one you select have been previously evaluated. After exploring similar system implementations, you will select one research goal and viewpoint to use in the evaluation.
Read the following three scenarios, and select the one that is of most interest to you:
Scenario 1:
Your hospital is implementing a new unified acute and ambulatory Electronic Health Record (EHR) system through which patient care documentation will occur. Interdisciplinary assessment forms (including nursing), clinical decision support, and medical notes will be documented in this system. The implementation of the system is anticipated to improve the hospital’s performance in a multitude of areas. In particular, it is hoped that the use of the EHR system will reduce the rate of patient safety events, improve the quality of care, deter sentinel events, reduce patient readmissions, and impact spending. The implementation of the EHR system is also intended to fulfill the “Meaningful Use” requirements stipulated in the Health Information Technology for Economic and Clinical Health (HITECH) Act. As the hospital’s lead nurse informaticist, you have been tasked with planning the evaluation of the EHR implementation.
Scenario 2:
As the lead nurse informaticist in your hospital, you have been given the task of planning an evaluation for a soon-to-be launched computerized provider order entry (CPOE) system. The CPOE system is designed to replace conventional methods of placing medication, laboratory, admission, referral, and radiology orders. CPOE systems enable health care providers to electronically specify orders, rather than rely on paper prescriptions, telephone calls, and faxes. The intended goal of a CPOE system is to improve safety by ensuring that orders are easily comprehensible through the use of evidence-based order sets. In addition, the CPOE system has the potential for improving workflow by avoiding duplicate orders and reducing the steps between those who place medical orders and their recipients.
Scenario 3:
You are the lead nurse informaticist in a large urban hospital that has recently implemented a new .
DISEASE INFERENCE FROM HEALTH-RELATED QUESTIONS VIA SPARSE DEEP LEARNINGvishnuRajan20
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Disease inference from health-related uestions vissparse deep learningvishnuRajan20
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Key Topics in Health Care Technology EvaluationThe amount of new i.docxsleeperfindley
Key Topics in Health Care Technology Evaluation
The amount of new information and data, and the number of available technologies are growing at an ever-accelerating rate. Did you know that during any given 24 hours, humanity generates enough new information to fill the Library of Congress 70 times (Smolan & Erwitt, 2012)? As a nurse informaticist, it is important to keep current on new developments in the field, but with the rapid pace of change, that effort can be overwhelming. It is easier to keep current with key trends if nurse informaticists focus on selected issues.
In this Discussion, you consider key topics in the field of health care technology. You then consider the different approaches you could take when designing an evaluation in these areas. For example, if you are interested in usability, your goal could be to determine if a system is user friendly from the viewpoint of a nurse. A different goal might be to determine if the location of the system facilitates ease of use from the viewpoint of physicians.
Note:
This Discussion serves as practice for the first part of your Evaluation Project. What you derive from your Discussion with colleagues will likely inform the work that you do in Part 1 of the Evaluation Project.
The Discussion focuses on the following major topics in the health care information field:
Implementing HIT Systems
Consumer health information
Computerized Physician Order Entry (CPOE)
Decision support systems
Electronic health records (EHR)
Tele-medicine and eHealth
Nursing documentation
Other Issues Related to the Use of HIT Systems
Interoperability
Unforeseen consequences
Usability
To prepare:
Select at least
two
topics from the
lists above
that are relevant to your current organization or that are of particular interest to you. Read the articles in this week’s Learning Resources that relate to these topics. Consider why these topics are of interest to you, what relevance they have to health care organizations, and how they impact your professional responsibilities. Choose one topic to be the focus of your Evaluation Project, and consider potential evaluation goals.
Determine the viewpoint from which you would approach the evaluation, and why.
By tomorrow, post a minimum of 550 words essay in APA format with a minimum of 3 references from the list of required resources below, that addresses the level one headings as numbered below:
1)
Post
the two topics you identified as most relevant to your organization or to you personally, and explain why you selected those topics.
2)
Identify the topic you selected for your Evaluation Project, and propose three potential evaluation goals for this topic.
3)
Identify the viewpoint you would use with each goal, and explain why.
Required Readings
Friedman, C. P., & Wyatt, J. C. (2010). Evaluation methods in biomedical informatics (2nd ed.). New York, NY: Springer Science+Business Media, Inc
.
Chapter 2, “Evaluation as a Field” (pp. 21–47)
This chapter defines.
The document defines several key terms related to medical informatics and computational biology. It provides definitions of medical informatics from various experts that describe it as the intersection of information science, computer science, social science, behavioral science and healthcare. It deals with optimizing the acquisition, storage, retrieval, and use of medical information. Bioinformatics is defined as using computers to collect, classify, store, and analyze biochemical and biological information, especially related to molecular genetics and genomics. Computational biology develops methods and software tools for understanding biological data by combining computer science, statistics, mathematics and engineering to study biological data.
The document discusses the importance of standardized coding systems in healthcare. As the healthcare system is fragmented, different specialties have developed their own terminology, which poses challenges for electronic medical records that require standardized codes. The implementation of standardized terminology allows for improved communication between healthcare professionals and across information systems. While coding standards are valuable for consistent high-quality care, some nurses do not fully understand their importance. The document examines why standardized nursing language is needed and whether it can be limited to specialties or applied more broadly across nursing practice.
The document discusses the importance of standardized coding systems in healthcare. As the healthcare system is fragmented, different specialties have developed their own terminology, which poses challenges for electronic medical records that require standardized codes. The implementation of standardized terminology allows for improved communication between healthcare professionals and across information systems. While coding standards are valuable for consistent high-quality care, some nurses do not fully understand their importance. The document examines why standardized nursing languages are needed and whether they should be used across all nursing practice or just within specialties.
Similar to IJCER (www.ijceronline.com) International Journal of computational Engineering research (20)
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Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
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Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
IJCER (www.ijceronline.com) International Journal of computational Engineering research
1. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5
Efficient Machine Learning Approach for identifying Disease-Treatment
Semantic Relations from Bio-Medical Sentences
1
Mr.P.Bhaskar, 2 Mr. Dr.E.Madhusudhana Reddy
1
M.C.A, (M.Tech) under guidance of,
2
M.Tech, Ph.D. Professor dept of CSE.
Abstract:
The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a
reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision
support, medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management
care. ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a
better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable
of identifying and disseminating healthcare information. It extracts sentences from published medical papers that mention
diseases and treatments, and identifies semantic relations that exist between diseases and treatments.. In addition to more
methodological settings in which we try to find the potential value of other types of representations, we would like to focus on
source data that comes from the web. Identifying and classifying medical-related information on the web is a challenge that
can bring valuable information to the research community and also to the end user. We also consider as potential future work
ways in which the framework’s capabilities can be used in a commercial recommender system and in integration in a new
EHR system.
Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be
integrated in an application to be used in the medical care domain. The potential value of this paper stands in the ML settings
that we propose and in the fact that we outperform previous results on the same data set.
Index Terms—Healthcare, machine learning, natural language processing
1. Introduction: -
PEOPLE care deeply about their health and want to be, now more than ever, in charge of their health and healthcare. Life is
more hectic than has ever been, the medicine that is practiced today is an Evidence-Based Medicine (hereafter, EBM) in which
medical expertise is not only based on years of practice but on the latest discoveries as well. Tools that can help us manage
and better keep track of our health such as Google Health1 and Microsoft HealthVault2 are reasons and facts that make people
more powerful when it comes to healthcare knowledge and management. The traditional healthcare system is also becoming
one that embraces the Internet and the electronic world. Electronic Health Records (hereafter, EHR) are becoming the standard
in the healthcare domain.
Researches and studies show that the potential benefits of having an EHR system are3: Health information recording and
clinical data repositories— immediate access to patient diagnoses, allergies, and lab test results that enable better and time-
efficient medical decisions. Medication management—rapid access to information regarding potential adverse drug reactions,
immunizations, supplies, etc; Decision support—the ability to capture and use quality medical data for decisions in the
workflow of health care.
In order to embrace the views that the EHR system has, we need better, faster, and more reliable access to
information. In the medical domain, the richest and most used source of information is Medline,4 a database of extensive life
science published articles. All research discoveries come and enter the repository at high rate (Hunter and Cohen [1]), making
the process of identifying and disseminating reliable information a very difficult task. The work that we present in this paper is
focused on two tasks: automatically identifying sentences published in medical abstracts (Medline) as containing or not
information about diseases and treatments, and automatically identifying semantic relations that exist between diseases and
treatments, as expressed in these texts. The second task is focused on three semantic relations: Cure, Prevent, and Side Effect.
2. Related Work:-
The most relevant related work is the work done by Rosario and Hearst [3]. The authors of this paper are the ones who created
and distributed the data set used in our research. The data set consists of sentences from Medline5 abstracts annotated with
Issn 2250-3005(online) September| 2012 Page 1425
2. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5
disease and treatment entities and with eight semantic relations between diseases and treatments. The main focus of their work
is on entity recognition for diseases and treatments.
The authors use Hidden Markov Models and maximum entropy models to perform both the task of entity recognition
and the relation discrimination. Their representation techniques are based on words in context, part of speech information,
phrases, and a medical lexical ontology—Mesh6 terms. Compared to this work, our research is focused on different
representation techniques, different classification models, and most importantly generates improved results with less annotated
data. The tasks addressed in our research are information extraction and relation extraction. From the wealth of research in
these domains, we are going to mention some representative works. The task of relation extraction or relation identification is
previously tackled in the medical literature, but with a focus on biomedical tasks: subcellularlocation (Craven, [4]), gene-
disorder association (Ray and Craven, [5]), and diseases and drugs (Srinivasan and Rindflesch, [6]). Usually, the data sets used
in biomedical specific tasks use short texts, often sentences. This is the case of the first two related works mentioned above.
The tasks often entail identification of relations between entities that co-occur in the same sentence.
3. The Proposed Approach:-
3.1 Tasks and Data Sets- The two tasks that are undertaken in this paper provide the basis for the design of an information
technology framework that is capable to identify and disseminate healthcare information. The first task identifies and extracts
informative sentences on diseases and treatments topics, while the second one performs a finer grained classification of these
sentences according to the semantic relations that exists between diseases and treatments.
The problems addressed in this paper form the building blocks of a framework that can be used by healthcare providers (e.g.,
private clinics, hospitals, medical doctors, etc.), companies that build systematic reviews8 (hereafter, SR), or laypeople who
want to be in charge of their health by reading the latest life science published articles related to their interests. The final
product can be envisioned as a browser plug-in or a desktop application that will automatically find and extract the latest
medical discoveries related to disease-treatment relations and present them to the user. The product can be developed and sold
by companies that do research in Healthcare Informatics, Natural Language Processing, and Machine Learning, and
companies that develop tools like Microsoft Health Vault. The value of the product from an e-commerce point of view stands
in the fact that it can be used in marketing strategies to show that the information that is presented is trustful (Medline articles)
and that the results are the latest discoveries. For any type of business, the trust and interest of customers are the key success
factors. Consumers are looking to buy or use products that satisfy their needs and gain their trust and confidence. Healthcare
products are probably the most sensitive to the trust and confidence of consumers. The first task (task 1 or sentence selection)
identifies sentences from Medline published abstracts that talk about diseases and treatments. We focus on three relations:
Cure, Prevent, and Side Effect, a subset of the eight relations that the corpus is annotated with. We decided to focus on these
three relations because these are most represented in the corpus while for the other five, very few examples are available.
Table 1 presents the original data set, the one used by Rosario and Hearst [3], that we also use in our research. The numbers in
parentheses represent the training and test set size. For example, for Cure relation, out of 810 sentences present in the data set,
648 are used for training and 162 for testing. The task of identifying the three semantic relations is addressed in two ways:
Setting 1. Three models are built. Each model is focused on one relation and can distinguish sentences that contain the relation
from sentences that do not. This setting is similar to a two-class classification task in which instances are labeled either with
the relation in question (Positive label) or with nonrelevant information (Negative label);
Setting 2. One model is built, to distinguish the three relations in a three-class classification task where each sentence is
labeled with one of the semantic relations
3.2 Classification Algorithms and Data Representations:- In ML, as a field of empirical studies, the acquired expertise and
knowledge from previous research guide the way of solving new tasks. The models should be reliable at identifying
informative sentences and discriminating disease- treatment semantic relations. The research experiments need to be guided
such that high performance is obtained. The experimental settings are directed such that they are adapted to the domain of
study (medical knowledge) and to the type of data we deal with (short texts or sentences), allowing for the methods to bring
improved performance. There are at least two challenges that can be encountered while working with ML techniques. One is
to find the most suitable model for prediction. The ML field offers a suite of predictive models (algorithms) that can be used
and deployed. The task of finding the suitable one relies heavily on empirical studies and knowledge expertise. The second
one is to find a good data representation and to do feature engineering because features strongly influence the performance of
the models. Identifying the right and sufficient features to represent the data for the predictive models, especially when the
source of information is not large, as it is the case of sentences, is a crucial aspect that needs to be taken into consideration.
Issn 2250-3005(online) September| 2012 Page 1426
3. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5
These challenges are addressed by trying various predictive algorithms, and by using various textual representation techniques
that we consider suitable for the task. As classification algorithms, we use a set of six representative models: decision-based
models (Decision trees), probabilistic models (Naı¨ve Bayes (NB) and Complement Naı¨ve Bayes (CNB), which is adapted for
text with imbalanced class distribution), adaptive learning (Ada- Boost), a linear classifier (support vector machine (SVM)
with polynomial kernel), and a classifier that always predicts the majority class in the training data (used as a baseline). We
decided to use these classifiers because they are representative for the learning algorithms in the literature and were shown to
work well on both short and long texts. Decision trees are decision-based models similar to the rule-based models that are used
in handcrafted systems, and are suitable for short texts. Probabilistic models, especially the ones based on the Naı¨ve Bayes
theory, are the state of the art in text classification and in almost any automatic text classification task. Adaptive learning
algorithms are the ones that focus on hard-to-learn concepts, usually underrepresented in the data, a characteristic that appears
in our short texts and imbalanced data sets. SVM-based models are acknowledged state-of-the-art classification techniques on
text. All classifiers are part of a tool called Weka.9 One can imagine the steps of processing the data (in our case textual
information—sentences) for ML algorithms as the steps required to obtain a database table that contains as many columns as
the number of features selected to represent the data, and as many rows as the number of data points from the collection
(sentences in our case). The most difficult and important step is to identify
Training Test
Positive negative Positive negative
Cure 554 531 276 266
Prevent 42 531 21 266
Side Effect 20 531 10 266
Fig 1: Data Sets Used for the Second Task
3.2.1 Bag-of-Words Representation:-
The bag-of-words (BOW) representation is commonly used for text classification tasks. It is a representation in which features
are chosen among the words that are present in the training data. Selection techniques are used in order to identify the most
suitable words as features. After the feature space is identified, each training and test instance is mapped to this feature
representation by giving values to each feature for a certain instance. Two most common feature value representations for
BOW representation are: binary feature values—the value of a feature can be either 0 or 1, where 1 represents the fact that the
feature is present in the instance and 0 otherwise; or frequency feature values—the value of the feature is the number of times
it appears in an instance, or 0 if it did not appear.
3.2.2 NLP and Biomedical Concepts Representation:-
The second type of representation is based on syntactic information: noun-phrases, verb-phrases, and biomedical concepts
identified in the sentences. In order to extract this type of information, we used the Genia11 tagger tool. The tagger analyzes
English sentences and outputs the base forms, part-of-speech tags, chunk tags, and named entity tags. The tagger is
specifically tuned for biomedical text such as Medline abstracts.
Fig. 1 presents an example of the output of the Genia tagger for the sentence: “Inhibition of NF-kappaB activation reversed the
anti-apoptotic effect of sochamaejasmin.” The noun and verb-phrases identified by the tagger are features used for the second
representation technique.
3.2.3 Medical Concepts (UMLS) Representation:- In order to work with a representation that provides features that are
more general than the words in the abstracts (used in the BOW representation), we also used the Unified Medical Language
system12 (hereafter, UMLS) concept representations. UMLS is a knowledge source developed at the US National Library of
Medicine (hereafter, NLM) and it contains a met thesaurus, a semantic network, and the specialist lexicon for biomedical
domain. The met thesaurus is organized around concepts and meanings; it links alternative names and views of the same
concept and identifies useful relationships between different concepts.
3.3 EHR System:-
An electronic health record (EHR) is an evolving concept defined as a systematic collection of electronic health
information about individual patients or populations. It is a record in digital format that is theoretically capable of being shared
across different health care settings. In some cases this sharing can occur by way of network-connected enterprise-wide
information systems and other information networks or exchanges.
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EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status,
laboratory test results, radiology images, vital signs, personal stats like age and weight, and billing information.
3.3.1 EHRs Respond to the Complex AMC
Environments:- The major value of integrated clinical systems is that they enable the capture of clinical data as a part of the
overall workflow. An EHR enables the administrator to obtain data for billing, the physician to see trends in the effectiveness
of treatments, a nurse to report an adverse reaction, and a researcher to analyze the efficacy of medications in patients with co-
morbidities. If each of these professionals works from a data silo, each will have an incomplete picture of the patient’s
condition. An EHR integrates data to serve different needs. The goal is to collect data once, then use it multiple times. EHRs
are used in complex clinical environments. Features and interfaces that are very appropriate for one medical specialty, such as
pediatrics, may be frustratingly unusable in another (such as the intensive care unit). The data presented, the format, the level
of detail, and the order of presentation may be remarkably different, depending on the service venue and the role of the user.
Scot M. Silverstein, MD, of Drexel University, stated “Clinical IT projects are complex social endeavors in unforgiving
clinical settings that happen to involve computers, as opposed to IT projects that happen to involve doctors.”
3.4 Physicians, Nurses, and Other Clinicians
EHR workflow implications for healthcare clinicians (physicians, nurses, dentists, nurse practitioners, etc.) may vary by
type of patient care facility and professional responsibility. However, the most cited changes EHRs foster involve increased
efficiencies, improved accuracy, timeliness, availability, and productivity (See references 1, 8, and 9 in the References
section).
Fig 2: Level of HER related technology used by teaching Hospitals
Clinicians in environments with EHRs spend less time updating static data, such as demographic and prior health history,
because these data are populated throughout the record and generally remain constant. Clinicians also have much greater
access to other automated information (regarding diseases, etc.), improved organization tools, and alert screens. Alerts are a
significant capacity of EHRs because they identify medication allergies and other needed reminders. For clinical researchers,
alerts can be established to assist with recruitment efforts by identifying eligible research participants. Workflow redesigns to
ensure increased efficiencies, to generate improvements in quality of care, and to realize the maximum benefits of an
automated environment. NIH Challenges that EHRs may present to workflow processes include: increased documentation
time (slow system response, system crashes, multiple screens, etc.), decreased interdisciplinary communication, and impaired
critical thinking through the overuse of checkboxes and other automated documentation. System crashes are particularly
problematic because clinicians, particularly at in-patient facilities, will not know what treatments are needed or if medications
are due. Interestingly, the national attention and rapid adoption of EHRs come at a time when the nursing industry is
experiencing a substantial decrease in workforce and an increase in workload. To help compensate for this workforce
discrepancy.
4. Conclusion:
The conclusions of our study suggest that domain-specific knowledge improves the results. Probabilistic models are stable and
reliable for tasks performed on short texts in the medical domain. The representation techniques influence the results of the
ML algorithms, but more informative representations are the ones that consistently obtain the best results. The first task that
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5. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5
we tackle in this paper is a task that has applications in information retrieval, information extraction, and text summarization.
We identify potential improvements in results when more information is brought in the representation technique for the task of
classifying short medical texts. We show that the simple BOW approach, well known to give reliable results on text
classification tasks, can be significantly outperformed when adding more complex and structured information from various
anthologies. The second task that we address can be viewed as a task that could benefit from solving the first task first. In this
study, we have focused on three semantic relations between diseases and treatments. Our work shows that the best results are
obtained when the classifier is not overwhelmed by sentences that are not related to the task. Also, to perform a triage of the
sentences (task 1) for a relation classification task is an important step. In Setting 1, we included the sentences that did not
contain any of the three relations in question and the results were lower than the one when we used models trained only on
sentences containing the three relations of interest. These discoveries validate the fact that it is crucial to have the first step to
weed out uninformative sentences, before looking deeper into classifying them. Similar findings and conclusions can be made
for the representation and classification techniques for task 2.
The above observations support the pipeline of tasks that we propose in this work. The improvement in results of 14 and 18
percentage points that we obtain for two of the classes in question shows that a framework in which tasks 1and 2 are used in
pipeline is superior to when the two tasks are solved in one step by a four-way classification. Probabilistic models combined
with a rich representation technique bring the best results.
References:-
[1]. L. Hunter and K.B. Cohen, “Biomedical Language Processing: What’s beyond PubMed?” Molecular Cell, vol. 21-5, pp.
589-594, 2006.
[2]. J. Ginsberg, H. Mohebbi Matthew, S.P. Rajan, B. Lynnette, S.S. Mark, and L. Brilliant, “Detecting Influenza Epidemics
Using Search Engine Query Data,” Nature, vol. 457, pp. 1012-1014, Feb. 2009.
[3]. B. Rosario and M.A. Hearst, “Semantic Relations in Bioscience Text,” Proc. 42nd Ann. Meeting on Assoc. for
Computational Linguistics, vol. 430, 2004.
[4]. M. Craven, “Learning to Extract Relations from Medline,” Proc.Assoc. for the Advancement of Artificial Intelligence,
1999.
[5]. S. Ray and M. Craven, “Representing Sentence Structure in Hidden Markov Models for Information Extraction,” Proc.
Int’l Joint Conf. Artificial Intelligence (IJCAI ’01), 2001.
[6]. P. Srinivasan and T. Rindflesch, “Exploring Text Mining from Medline,” Proc. Am. Medical Informatics Assoc.
(AMIA) Symp., 2002
[7] M. Craven, “Learning to Extract Relations from Medline,” Proc. Assoc. for the Advancement of Artificial Intelligence,
1999.
[8] I. Donaldson et al., “PreBIND and Textomy: Mining the Biomedical Literature for Protein-Protein Interactions Using a
Support Vector Machine,” BMC Bioinformatics, vol. 4, 2003.
[9] AUTHORS DESCRIPTION
P. Bhaskar, received MCA from JNTU, Hyderabad. Currently he is doing M.Tech Academic Project from Madanapalle
Institute of Technology and Science, Madanapalle, Andhra Pradesh, India. His Research interest areas are Data warehousing
and Mining & Mobile Computing. He attended so many workshops and National and International conferences.
[10] AUTHORS DESCRIPTION
Dr.E.Madhusudhana Reddy, received M.C.A from S.V. University, Tirupati, M.Tech (IT) from Punjabi
University, Patiala, M.Phil (Computer Science) from Madurai Kamaraj University, Madurai, and PhD from S.V. University,
Tirupati. Currently he is working as Head, Department of Information Technology, Madanapalle Institute of Technology
and Science, Madanapalle, Andhra Pradesh, India. His research interests lie in the areas of E-Commerce, Cryptography,
Network Security and Data Mining. He has published 27 research papers in national/International journals and conferences.
He is a Research Supervisor for Periyar University, Salem, India guiding for M.Phil. Also he is life member of Indian
Society of Technical Education of India (ISTE), life member of Cryptography Research Society of India (CRSI) and Fellow in ISET, India.
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