Illustrating how a well planned data infrastructure designed for real-time and continuous learning can have multiple advantages, facilitating better preventive strategies
The Quahog Decision Platform aims to save over a million lives each year by improving medical diagnosis and decision-making. It does this by creating a centralized data infrastructure that collects and unifies medical data from various sources. This unified data set allows machine learning algorithms to analyze relationships between all relevant health parameters and arrive at the most accurate diagnosis within minutes. The platform also aims to enable personalized treatment, faster diagnosis, preventive care through predictions, monitoring of treatment effectiveness, and insights to support new medical research and innovations.
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.
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.
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.
IRJET- Heart Disease Prediction and RecommendationIRJET Journal
This document describes a study that developed a machine learning model to predict heart disease risk and provide recommendations. The study used a decision tree algorithm and the Cleveland heart disease dataset to train a model. The model takes in 14 clinical attributes to predict the risk of heart disease on a scale of 0 to 1. It then provides control measure recommendations based on the predicted risk level to help users reduce their risk. The system was designed to be implemented as an Android application for users to input their data and receive the prediction and recommendations.
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 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.
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.
The Quahog Decision Platform aims to save over a million lives each year by improving medical diagnosis and decision-making. It does this by creating a centralized data infrastructure that collects and unifies medical data from various sources. This unified data set allows machine learning algorithms to analyze relationships between all relevant health parameters and arrive at the most accurate diagnosis within minutes. The platform also aims to enable personalized treatment, faster diagnosis, preventive care through predictions, monitoring of treatment effectiveness, and insights to support new medical research and innovations.
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.
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.
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.
IRJET- Heart Disease Prediction and RecommendationIRJET Journal
This document describes a study that developed a machine learning model to predict heart disease risk and provide recommendations. The study used a decision tree algorithm and the Cleveland heart disease dataset to train a model. The model takes in 14 clinical attributes to predict the risk of heart disease on a scale of 0 to 1. It then provides control measure recommendations based on the predicted risk level to help users reduce their risk. The system was designed to be implemented as an Android application for users to input their data and receive the prediction and recommendations.
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 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.
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.
Comparing Data Mining Techniques used for Heart Disease PredictionIRJET Journal
This document compares various data mining techniques for predicting heart disease, including neural networks, decision trees, and Naive Bayes classification. It analyzes past research applying these techniques to heart disease data and finds that neural networks achieved the highest accuracy of 100% when using 15 attributes. Decision tree techniques like C4.5, ID3, CART and J48 also performed well with accuracies over 90%. Naive Bayes classification achieved average accuracy of around 90%. The document concludes neural networks are the most effective technique for heart disease prediction when sufficient attributes are available.
IRJET- Prediction and Analysis of Heart Disease using SVM AlgorithmIRJET Journal
This document describes a study that uses a support vector machine (SVM) algorithm to predict heart disease based on patient data. The study uses a dataset of 1000 patient records with 8 attributes related to risk factors for heart disease. The SVM algorithm is applied to identify patterns in the data and classify patients as having heart disease or not. It aims to find the optimal decision boundary between the two classes to minimize classification errors. The results show that the SVM technique can accurately predict heart disease based on the risk factor attributes in the patient data.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
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.
Performance evaluation of random forest with feature selection methods in pre...IJECEIAES
Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes.
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.
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
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.
This document discusses the application of machine learning in healthcare. It provides an overview of machine learning and data science concepts and methodologies like the CRISP-DM process. It also discusses challenges with non-communicable diseases and opportunities for applying machine learning to areas like precision medicine, disease diagnosis, and clinical trials optimization using diverse healthcare data sources. Machine learning can help address issues like reducing healthcare costs and improving outcomes for conditions like diabetes and cardiovascular disease.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
Diabetes prediction using machine learningdataalcott
This document discusses a proposed system to classify and predict diabetes using machine learning and deep learning algorithms. The objectives are to classify the PIMA Indian diabetes dataset and design an interactive application where users can input data to get a prediction. The proposed system uses support vector machine (SVM) for machine learning and neural networks for deep learning. It aims to improve accuracy over existing systems by using deep learning techniques. The methodology involves collecting a dataset, preprocessing, splitting for training and testing, applying algorithms, and evaluating results.
IRJET - E-Health Chain and Anticipation of Future DiseaseIRJET Journal
The document proposes an E-Health Chain system that uses machine learning algorithms to predict future diseases and maintain electronic health records more efficiently than traditional paper-based systems. Key features include digital prescriptions that allow patients to purchase medicines using a unique ID, electronic health records that give doctors access to patient data from remote locations, emergency response features like ambulance dispatch, and medication reminders for patients. The system aims to help patients take preventive measures by predicting future diseases using algorithms trained on historical health data. A literature review covers previous research on disease prediction using decision trees, neural networks, and other machine learning methods applied to medical datasets.
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.
dkNET Webinar: Multi-Omics Data Integration for Phenotype Prediction of Type-...dkNET
Abstract
Omics techniques (e.g., i.e., transcriptomics, genomics, and epigenomics) report quantitative measures of more than tens of thousands of biological features and provide a more comprehensive molecular perspective of studied diabetes mechanisms compared to transitional approaches. Identifying representative molecular signatures from the tremendous number of biological features becomes a central problem in utilizing the data for clinical decision-making. Exploring the complex causal relations of the identified representative molecular signatures and diabetes phenotypes can be the most effective and efficient ways to improve the understanding of diabetes and assess the cause of diabetes for the new patients with already collected data influencing (e.g., TEDDY project). However, due to the unavoidable patient heterogeneity, statistical randomness, and experimental noise in the high-dimension, low-sample-size omics data of the diabetic patients, utilizing the available data for clinical decision-making remains an ongoing challenge for many researchers. To overcome the limitations, in this study we developed (1) a generative adversarial network (GAN)-based model to generate synthetic omics data for the samples with few omics profiles available; (2) a deep learning-based fusion network model for phenotype prediction of type-1 diabetes; (3) a long short-term memory (LSTM)-based model for predicting outcomes of islet autoantibody and persistent positivity. The models are tested on the multi-omics data in TEDDY project.
Presenter: Wei Zhang, Ph.D. Assistant Professor, Department of Computer Science & Genomics and Bioinformatics Cluster, University of Central Florida
Upcoming webinars schedule: https://dknet.org/about/webinar
Human heart can be described as a compound body organ contains muscles together with
biological nerves. Human heart pumps nearly 5 litre of blood in the body providing the human body
with renewed material [6]. If operation of heart is not proper, it will affect the other body parts of
human such as brain, kidney etc. various study revealed that heart disease have emerged as the
number one killer in world. About 25 per cent of deaths in the age group of 25-69 years occur
because of heart disease. There are number of factors, which increase the risk of heart disease such
as smoking, cholesterol, high blood pressure, obesity and low physical exercise etc. The World
Health Organisation (WHO) has estimated that 12 million deaths occur worldwide, every year due to
heart diseases. WHO estimated by 2030, almost 23.6 million people will die due to Heart
disease.Cardiovascular disease includes coronary heart disease (CHD), cerebrovascular disease
(stroke), Hypertensive heart disease, congenital heart disease, peripheral artery disease, rheumatic
heart disease, inflammatory heart disease [5].
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
IRJET- Diabetes Prediction using Machine LearningIRJET Journal
This document discusses predicting diabetes using machine learning algorithms. It analyzes the Pima Indian diabetes dataset using Support Vector Machine (SVM), K-Nearest Neighbors, and Decision Tree algorithms. SVM achieved the highest accuracy of 80% for predicting whether a patient has diabetes. Key features like glucose level and body mass index were most important for prediction. A GUI was created to allow users to enter patient data and predict diabetes status using the SVM model trained on the dataset.
One of the major purposes manufacturers incorporate AI or ML in their applications is to ease software computations and to predict precise results. I think compared to any other application, a medical application requires a lot of precise computations and therefore, AI is a perfect solution to enhance performance and productivity. While reading the health-tech news, I came across recent research in this regard, the use of AI in predicting a potential stroke or cardiac arrest. ..
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
IRJET- Diabetes Diagnosis using Machine Learning AlgorithmsIRJET Journal
This document presents research on using machine learning algorithms to diagnose diabetes. The researchers collected a dataset of 15,000 patient records from the National Institute of Diabetes and Digestive and Kidney Diseases. They analyzed the dataset and used machine learning algorithms like decision trees, naive Bayes, support vector machines, and k-nearest neighbors to build predictive models. The models were evaluated based on accuracy and other performance metrics. The naive Bayes classifier achieved the highest accuracy of 72% in predicting whether patients had diabetes. The research aims to develop a machine learning system that can predict diabetes early to help treat patients before the disease becomes critical.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Multi Disease Detection using Deep LearningIRJET Journal
1) The document proposes a system for multi-disease detection using deep learning that could provide early detection of chronic diseases like heart disease, cancer, and diabetes from medical data and save lives.
2) It reviews literature on disease prediction using machine learning algorithms like CNN, KNN, decision trees, and support vector machines. CNN showed slightly better accuracy than KNN for general disease detection.
3) The proposed system would use deep learning models to detect and classify diseases from medical images and data with high accuracy, helping doctors verify test results and enhancing their experience with diseases. It aims to reduce the costs of diagnostic testing for chronic conditions.
Comparing Data Mining Techniques used for Heart Disease PredictionIRJET Journal
This document compares various data mining techniques for predicting heart disease, including neural networks, decision trees, and Naive Bayes classification. It analyzes past research applying these techniques to heart disease data and finds that neural networks achieved the highest accuracy of 100% when using 15 attributes. Decision tree techniques like C4.5, ID3, CART and J48 also performed well with accuracies over 90%. Naive Bayes classification achieved average accuracy of around 90%. The document concludes neural networks are the most effective technique for heart disease prediction when sufficient attributes are available.
IRJET- Prediction and Analysis of Heart Disease using SVM AlgorithmIRJET Journal
This document describes a study that uses a support vector machine (SVM) algorithm to predict heart disease based on patient data. The study uses a dataset of 1000 patient records with 8 attributes related to risk factors for heart disease. The SVM algorithm is applied to identify patterns in the data and classify patients as having heart disease or not. It aims to find the optimal decision boundary between the two classes to minimize classification errors. The results show that the SVM technique can accurately predict heart disease based on the risk factor attributes in the patient data.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The document describes a predictive data mining algorithm for medical diagnosis that uses support vector machine (SVM) and random forest (RF) algorithms. It analyzes diabetes, kidney, and liver disease databases using these techniques. The proposed algorithm applies SVM and RF to the datasets and achieves high prediction accuracies of 99.35%, 99.37%, and 99.14% for diabetes, kidney, and liver diseases respectively. It also compares the performance of SVM and RF based on metrics like precision, recall, accuracy, and execution time.
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.
Performance evaluation of random forest with feature selection methods in pre...IJECEIAES
Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes.
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.
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
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.
This document discusses the application of machine learning in healthcare. It provides an overview of machine learning and data science concepts and methodologies like the CRISP-DM process. It also discusses challenges with non-communicable diseases and opportunities for applying machine learning to areas like precision medicine, disease diagnosis, and clinical trials optimization using diverse healthcare data sources. Machine learning can help address issues like reducing healthcare costs and improving outcomes for conditions like diabetes and cardiovascular disease.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
Diabetes prediction using machine learningdataalcott
This document discusses a proposed system to classify and predict diabetes using machine learning and deep learning algorithms. The objectives are to classify the PIMA Indian diabetes dataset and design an interactive application where users can input data to get a prediction. The proposed system uses support vector machine (SVM) for machine learning and neural networks for deep learning. It aims to improve accuracy over existing systems by using deep learning techniques. The methodology involves collecting a dataset, preprocessing, splitting for training and testing, applying algorithms, and evaluating results.
IRJET - E-Health Chain and Anticipation of Future DiseaseIRJET Journal
The document proposes an E-Health Chain system that uses machine learning algorithms to predict future diseases and maintain electronic health records more efficiently than traditional paper-based systems. Key features include digital prescriptions that allow patients to purchase medicines using a unique ID, electronic health records that give doctors access to patient data from remote locations, emergency response features like ambulance dispatch, and medication reminders for patients. The system aims to help patients take preventive measures by predicting future diseases using algorithms trained on historical health data. A literature review covers previous research on disease prediction using decision trees, neural networks, and other machine learning methods applied to medical datasets.
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.
dkNET Webinar: Multi-Omics Data Integration for Phenotype Prediction of Type-...dkNET
Abstract
Omics techniques (e.g., i.e., transcriptomics, genomics, and epigenomics) report quantitative measures of more than tens of thousands of biological features and provide a more comprehensive molecular perspective of studied diabetes mechanisms compared to transitional approaches. Identifying representative molecular signatures from the tremendous number of biological features becomes a central problem in utilizing the data for clinical decision-making. Exploring the complex causal relations of the identified representative molecular signatures and diabetes phenotypes can be the most effective and efficient ways to improve the understanding of diabetes and assess the cause of diabetes for the new patients with already collected data influencing (e.g., TEDDY project). However, due to the unavoidable patient heterogeneity, statistical randomness, and experimental noise in the high-dimension, low-sample-size omics data of the diabetic patients, utilizing the available data for clinical decision-making remains an ongoing challenge for many researchers. To overcome the limitations, in this study we developed (1) a generative adversarial network (GAN)-based model to generate synthetic omics data for the samples with few omics profiles available; (2) a deep learning-based fusion network model for phenotype prediction of type-1 diabetes; (3) a long short-term memory (LSTM)-based model for predicting outcomes of islet autoantibody and persistent positivity. The models are tested on the multi-omics data in TEDDY project.
Presenter: Wei Zhang, Ph.D. Assistant Professor, Department of Computer Science & Genomics and Bioinformatics Cluster, University of Central Florida
Upcoming webinars schedule: https://dknet.org/about/webinar
Human heart can be described as a compound body organ contains muscles together with
biological nerves. Human heart pumps nearly 5 litre of blood in the body providing the human body
with renewed material [6]. If operation of heart is not proper, it will affect the other body parts of
human such as brain, kidney etc. various study revealed that heart disease have emerged as the
number one killer in world. About 25 per cent of deaths in the age group of 25-69 years occur
because of heart disease. There are number of factors, which increase the risk of heart disease such
as smoking, cholesterol, high blood pressure, obesity and low physical exercise etc. The World
Health Organisation (WHO) has estimated that 12 million deaths occur worldwide, every year due to
heart diseases. WHO estimated by 2030, almost 23.6 million people will die due to Heart
disease.Cardiovascular disease includes coronary heart disease (CHD), cerebrovascular disease
(stroke), Hypertensive heart disease, congenital heart disease, peripheral artery disease, rheumatic
heart disease, inflammatory heart disease [5].
Data Science Deep Roots in Healthcare IndustryDinesh V
Data Science transforms the healthcare industry with impeccable solutions that can improve patient care through EHRs, medical imaging, drug discovery, predictive medicines and genetics and genomics.
IRJET- Diabetes Prediction using Machine LearningIRJET Journal
This document discusses predicting diabetes using machine learning algorithms. It analyzes the Pima Indian diabetes dataset using Support Vector Machine (SVM), K-Nearest Neighbors, and Decision Tree algorithms. SVM achieved the highest accuracy of 80% for predicting whether a patient has diabetes. Key features like glucose level and body mass index were most important for prediction. A GUI was created to allow users to enter patient data and predict diabetes status using the SVM model trained on the dataset.
One of the major purposes manufacturers incorporate AI or ML in their applications is to ease software computations and to predict precise results. I think compared to any other application, a medical application requires a lot of precise computations and therefore, AI is a perfect solution to enhance performance and productivity. While reading the health-tech news, I came across recent research in this regard, the use of AI in predicting a potential stroke or cardiac arrest. ..
Heart Disease Prediction using Data MiningIRJET Journal
This document describes a study that uses data mining techniques like neural networks and genetic algorithms to predict heart disease based on major risk factors. The proposed system initializes neural network weights using a genetic algorithm for feature selection and classification to build an intelligent clinical decision support system. It analyzes heart disease risk factors like age, cholesterol, blood pressure, smoking status and diabetes using a neuro-fuzzy model optimized with a genetic algorithm. The system is able to predict heart disease with 89% accuracy and can help detect the disease early to improve treatment outcomes.
IRJET- Diabetes Diagnosis using Machine Learning AlgorithmsIRJET Journal
This document presents research on using machine learning algorithms to diagnose diabetes. The researchers collected a dataset of 15,000 patient records from the National Institute of Diabetes and Digestive and Kidney Diseases. They analyzed the dataset and used machine learning algorithms like decision trees, naive Bayes, support vector machines, and k-nearest neighbors to build predictive models. The models were evaluated based on accuracy and other performance metrics. The naive Bayes classifier achieved the highest accuracy of 72% in predicting whether patients had diabetes. The research aims to develop a machine learning system that can predict diabetes early to help treat patients before the disease becomes critical.
An AI-based Decision Platform built using unified data model, incorporating systems biology topics for unit analysis using semi-supervised learning models
Multi Disease Detection using Deep LearningIRJET Journal
1) The document proposes a system for multi-disease detection using deep learning that could provide early detection of chronic diseases like heart disease, cancer, and diabetes from medical data and save lives.
2) It reviews literature on disease prediction using machine learning algorithms like CNN, KNN, decision trees, and support vector machines. CNN showed slightly better accuracy than KNN for general disease detection.
3) The proposed system would use deep learning models to detect and classify diseases from medical images and data with high accuracy, helping doctors verify test results and enhancing their experience with diseases. It aims to reduce the costs of diagnostic testing for chronic conditions.
Enhanced Detection System for Trust Aware P2P Communication NetworksEditor IJCATR
Botnet is a number of computers that have been set up to forward transmissions to other computers unknowingly to the user
of the system and it is most significant to detect the botnets. However, peer-to-peer (P2P) structured botnets are very difficult to detect
because, it doesn’t have any centralized server. In this paper, we deliver an infrastructure of P2P that will improve the trust of the peers
and its data. In order to identify the botnets we provide a technique called data provenance integrity. It will ensure the correct origin or
source of information and prevents opponents from using host resources. A reputation based trust model is used for selecting the
trusted peer. In this model, each peer has a reputation value which is calculated based on its past activity. Here a hash table is used for
efficient file searching and data stored in it is based on the reputation value.
C omparative S tudy of D iabetic P atient D ata’s U sing C lassification A lg...Editor IJCATR
Data mining refers to extracting knowledge from large amount of data. Real life data mining approaches are
interesting because they often present a different se
t of problems for
diabetic
patient’s
data
.
The
research area to solve
various problems and classification is one of main problem in the field. The research describes algorithmic discussion of J48
,
J48 Graft, Random tree, REP, LAD. Here used to compare the
performance of computing time, correctly classified
instances, kappa statistics, MAE, RMSE, RAE, RRSE and
to find the error rate measurement for different classifiers in
weka .In this paper the
data
classification is diabetic patients data set is develope
d by collecting data from hospital repository
consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine t
ests.
Weka tool is used to classify the data is evaluated using 10 fold cross validat
ion and the results are compared. When the
performance of algorithms
,
we found J48 is better algorithm in most of the cases
Comparative Study of Diabetic Patient Data’s Using Classification Algorithm i...Editor IJCATR
Data mining refers to extracting knowledge from large amount of data. Real life data mining approaches are
interesting because they often present a different set of problems for diabetic patient’s data. The research area to solve
various problems and classification is one of main problem in the field. The research describes algorithmic discussion of J48,
J48 Graft, Random tree, REP, LAD. Here used to compare the performance of computing time, correctly classified
instances, kappa statistics, MAE, RMSE, RAE, RRSE and to find the error rate measurement for different classifiers in
weka .In this paper the data classification is diabetic patients data set is developed by collecting data from hospital repository
consists of 1865 instances with different attributes. The instances in the dataset are two categories of blood tests, urine tests.
Weka tool is used to classify the data is evaluated using 10 fold cross validation and the results are compared. When the
performance of algorithms, we found J48 is better algorithm in most of the cases.
DATA MINING CLASSIFICATION ALGORITHMS FOR KIDNEY DISEASE PREDICTION IJCI JOURNAL
Data mining is a non-trivial process of categorizing valid, novel, potentially useful and ultimately understandable patterns in data. In terms, it accurately state as the extraction of information from a huge database. Data mining is a vital role in several applications such as business organizations, educational institutions, government sectors, health care industry, scientific and engineering. . In the health care
industry, the data mining is predominantly used for disease prediction. Enormous data mining techniques are existing for predicting diseases namely classification, clustering, association rules, summarizations, regression and etc. The main objective of this research work is to predict kidney diseases using classification algorithms such as Naïve Bayes and Support Vector Machine. This research work mainly
focused on finding the best classification algorithm based on the classification accuracy and execution time performance factors. From the experimental results it is observed that the performance of the SVM is better than the Naive Bayes classifier algorithm.
A Web Mobile Decision Support System To Improve Medical Diagnosis Using A Com...Wendy Berg
This document describes a proposed web/mobile decision support system that uses a combination of K-means clustering and fuzzy logic to improve medical diagnosis. The system would collect medical data, cluster it using K-means, and then use a fuzzy logic expert system to map symptoms to diagnoses based on the clustered data and medical knowledge. It is intended to provide detailed explanations for how it reaches diagnoses to help users understand the decision making process. Initial results found the system achieved 90% accuracy and 92.9% F-score in medical diagnosis.
A web/mobile decision support system to improve medical diagnosis using a com...TELKOMNIKA JOURNAL
This research provides a system that integrates the work of data mining and expert system for different tasks in the process of medical diagnosis, and provides detailed steps to the process of reaching a diagnosis based on the described symptoms and mapping them with existing diagnosis available on the web or on a cloud of medical knowledge based, aggregate these data in a fuzzy manner and produce a satisfactory diagnosis of the persisting problem. The mobile phone interface would make the system user-friendly and provides mobility and accessibility to the user, while posting updates and reading in details the steps that led to the decision or diagnosis that is reached by the K-mean and the fuzzy logic inference engine. The achieved results indicate a promising diagnosis performance of the system as it achieved 90% accuracy and 92.9% F-Score.
Patterns discovered from based on collected molecular profiles of patient tumour samples, and also clinical metadata, could be used to provide personalized cancer treatment to patients with
similar molecular subtypes. Computational algorithms for cancer diagnosis, prognosis, and therapeutics that can recognize specific functions and aid in classifiers based on a plethora of
publicly accessible cancer research outcomes are needed. Machine learning, a branch of artificial intelligence, has a great deal of potential for problem solving in cryptic cancer
datasets, as per a literature study. We focus on the new state of machine learning applications in cancer research in this study, illustrating trends and analysing major accomplishments,
roadblocks, and challenges along the way to clinic implementation. In the context of noninvasive treating cancer using diet-based and natural biomarkers, we propose a novel machine learning algorithm.
This document discusses data mining algorithms for clustering healthcare data streams. It provides an overview of the K-means and D-stream algorithms, and proposes a framework for comparing them on healthcare datasets. The framework involves feature extraction from physiological signals, calculating risk components, and applying the K-means and D-stream algorithms to cluster the data. The results would show the effectiveness and limitations of each algorithm for clustering streaming healthcare data.
Predicting Heart Disease Using Machine Learning Algorithms.IRJET Journal
This document summarizes a research paper that predicts heart disease using machine learning algorithms. It compares the performance of three algorithms - logistic regression, decision trees, and random forests - on a heart disease dataset. Logistic regression achieved the highest accuracy at 92%, outperforming decision trees and random forests. The paper outlines developing a heart disease prediction web application using logistic regression that allows users to input their medical details and get a prediction of their heart disease risk level.
This document presents a health analyzer system that uses machine learning to predict multiple diseases from user-input data. The system was designed to predict diabetes, stroke, breast cancer, fetal health, liver disease, and heart disease. It uses various machine learning algorithms like random forest, SVM, logistic regression, naive bayes and decision trees. Models for each disease were trained on different datasets and the best performing algorithm was selected for each disease. A Flask API with user interfaces was created to allow users to input data and receive predictions. The system aims to provide a cost-effective solution compared to separate systems for each disease. It analyzes diseases by considering all relevant parameters to detect effects more accurately.
This document discusses using data mining techniques like association rule mining and improved apriori algorithm with fuzzy logic to develop an expert system that can predict the risk of osteoporosis based on a patient's clinical data and history. It aims to help doctors make more informed decisions early on to prevent osteoporosis. The system would find relationships between various risk factors and diagnose osteoporosis severity to identify at-risk patients before costly tests. Literature on using different algorithms like decision trees and neural networks for medical diagnosis and predicting osteoporosis risk is also reviewed.
IRJET- Drugs Selection in Medical Field: A SurveyIRJET Journal
This document discusses using fuzzy logic systems to analyze healthcare databases and recommend drugs for patients. It first reviews related works applying fuzzy logic to medical domains like osteoporosis detection and medicine recommendation. It then proposes a new system using fuzzy rules and a knowledge database of patient diagnoses and prescribed drugs to suggest medications for clinicians. The system aims to help clinicians, especially less experienced ones, select drugs for patients with multiple conditions.
This document summarizes a research paper on developing a cloud-based health prediction system. The system allows users to enter their health issues and details like weight and height online. It then provides an accurate health prediction by matching the user's data to an analysis database. The cloud-based system is designed to be user-friendly and accessible from anywhere at any time. It aims to help users identify potential health problems early without visiting a doctor. The system architecture uses HTML, CSS, JavaScript, PHP and a MySQL database. It flows user data through registration, selecting health details, and logout for security.
Team Sol2 01 Health Care Informatics Power PointMessner Angie
The document discusses clinical information systems and their components. It provides an overview of electronic health records and describes key parts of a clinical information system including health information, order entry, decision support, and clinical documentation. It also discusses clinical decision making systems and their importance in reducing variation, costs, and improving diagnosis. Safety, education and costs related to clinical information systems are also evaluated.
Redesigning the healthcare with artificial intelligence, genomics & neuroscienceArtivatic.ai
HEALTHCARE WHITEPAPER BY ARTIVATIC DATA LABS PRIVATE LIMITED
Healthcare in today’s world has not changed in terms of method of diagnosis where the doctor analyses the patient’s history along with historical records of symptoms to their diagnosis, keeping in mind the current practices involved in the treatment. Usually going through multiple tests and a process of elimination, the process is hectic and more often than not prone to human error. It is not possible for any doctor to analyse every bit of data available in relation to a patient which may include the genetic code etc. Nor is it possible for them to keep track of all historical cases where similar symptoms may have been shown. This is where the application of AI and ML are crucial. They streamline the process and reduce human error while considering all the data available. With the use of AI, the doctor could automatically get recommendations on what kind of diseases could be causing the symptoms shown. Or the patients could be suggested the correct doctor based on their personal preferences and symptoms shown.
Artificial Intelligence, Machine Learning, Genomic, Neuroscience, Diseases
MULTI MODEL DATA MINING APPROACH FOR HEART FAILURE PREDICTIONIJDKP
Developing predictive modelling solutions for risk estimation is extremely challenging in health-care
informatics. Risk estimation involves integration of heterogeneous clinical sources having different
representation from different health-care provider making the task increasingly complex. Such sources are
typically voluminous, diverse, and significantly change over the time. Therefore, distributed and parallel
computing tools collectively termed big data tools are in need which can synthesize and assist the physician
to make right clinical decisions. In this work we propose multi-model predictive architecture, a novel
approach for combining the predictive ability of multiple models for better prediction accuracy. We
demonstrate the effectiveness and efficiency of the proposed work on data from Framingham Heart study.
Results show that the proposed multi-model predictive architecture is able to provide better accuracy than
best model approach. By modelling the error of predictive models we are able to choose sub set of models
which yields accurate results. More information was modelled into system by multi-level mining which has
resulted in enhanced predictive accuracy.
Similar to Data Infrastructure for Real-time Analysis to provide Health Insights (20)
Monitor and Engage Clinical Trial Participants for better outcomeQuahog Life Sciences
Quahog platform provides a comprehensive solution for Clinical Trial administrators to collect and integrate data from devices, so that participant health parameters can be monitored daily and get rich insights on trial effectiveness. Monitoring in real-time can also allow them to handle adverse events more effectively
The key to longevity in trying to live with Diabetes is in precise management and that is hindered by meds with side effects or lack of awareness among patients or it could be lack of everyday patient compliance
This document summarizes a healthcare data unification solution called Quahog's HealthDSTM. The solution aims to solve the healthcare data disconnect by aggregating and normalizing data across disparate systems to create a unified patient record. This allows for seamless access to current and historical patient information. The unified data foundation also supports analytics, workflows, and real-time monitoring to generate insights and recommendations. This assists with continuous patient care and helps streamline operational processes. The expected benefits include deeper insights for improved decision-making, faster access to real-time insights, and cost savings from reduced data storage needs and advanced automation.
Quahog Life Sciences is building an AI based Healthcare Decision System (Health DS) that promises to take the accuracy of health care decisions to a new level using machine learning and advanced analytics.
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Quahog Data Visualization is a module that allows medical enterprises like hospitals, pharmaceutical companies, and bioresearch organizations to build insights and decision dashboards from their data on a unified platform. It features data import, transformation, analysis and visualization capabilities. Pre-built models can be configured to extract behavioral patterns, perform collaborative filtering, and recognize named entities for information extraction. The platform provides flexibility in data organization and integration with other modules for runtime reporting and insights. Its advantages include time savings, inbuilt analytical models, and instant notifications.
The document discusses the goals and strategies of Quahog Life Sciences to extend human lifespan. Their goal is to delay aging processes and find solutions to diseases, physical damage, and misdiagnosis that lead to death. They propose strategies like expanding biotechnology to restore youth and health, replacing organs with artificial ones, restoring youthful blood factors, and uploading memories to new bodies. They cite evidence that other species and some human populations have lived over 120 years. Achieving their goals would require developing an artificial intelligence system to analyze the many factors influencing lifespan and health.
The document provides an overview of solutions from Quahog Life Sciences including data management, security, analysis, visualization, and applications. The platform allows merging of multiple data sources, creation of a logical data model, and organization of patient data. Advanced encryption is used to securely share data. The platform supports machine learning using a recursive neural network and analytics models. Use cases described include pattern discovery in cancer research and influencer detection in cellular research. Visualization capabilities include interactive dashboards with multiple chart types. Additional applications include bot assistance for diabetologists and physicians.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
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Topics covered:
• The role of a steering committee
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Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
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In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
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5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
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What to expect
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Data Infrastructure for Real-time Analysis to provide Health Insights
1. The ‘right’ data infrastructure can
save a million lives across the
globe, every year.
2. Every year, more than a million lives are lost, due to misdiagnosis
Misdiagnosis is a resultant of bad decisions which occurs due to
human assumptions based on incomplete data, resulting in death.
Another important factor in the decision cycle is time. Time taken to
diagnose the right condition can be delayed due to wrong
assumptions and iterations rising from incomplete data.
Lack of accuracy and time taken to relevant decision making can
therefore attributed as the primary reasons for these deaths
We believe the right data infrastructure for all medically relevant data
can help solve both lack of accuracy and lost time, both issues critical
to preventing loss of life and death
3. Today, we are in possession of both the technology and the requisite
knowledge to derive ‘accurate decisions’. By applying analytical
models to a full data set, we can arrive at the most possible scenario
within minutes
Quahog Decision Platform
to address ‘decision’ and ‘time’ related issues to help arrive at the right
solution. The centralized platform acts as the main hub, where data
from various sources is tagged and unified. This unified data becomes
the foundation for learning and analysis.
Browse further to understand the various facets of the platform
4. Data Infrastructure
The design creates a knowledge network that can allow machine algorithms to learn,
predict and output the most accurate possibilities. The knowledge network
encompasses every data parameter within the scope of modern system biology
allowing for analysis at a molecular level.
5. # Compute
Analyzing Medical Data involves comparing patient inputs with generic constants of
the human body. The compute server houses the range(weights) for each molecular
data parameter. Eg; Determination of the diabetic status of a patient when their
blood sugar count is compared against a range scale
The compute stack consists of a semantic graph of
data parameters from the databases of system
biology. Each of the data nodes maintain a range
scale. The relationship between each data node of
the hierarchy depicts the pattern (signaling
pathway)
6. # Precision Medicine
The semantic graph facilitates accuracy by using a holistic approach in analyzing
data by incorporating every influencing data parameter. This helps unearth every
possible scenario of a given data parameter. The associated weights help to
determine the accuracy of the possibility
Based on user data, the compute stack will gather
derived constants that is deduced from single data
stack across millions of users. The compute stack is
continuously learning from patient data and
improving the accuracy of the range scale
7. # Data Collection – Silos
The vast data required for analysis is collected across many devices and stored in
different silos. For holistic analysis we require every data parameter of a patient
together. Eg; A laboratory report and an MRI report are typically lying in two different
silos
Currently analysis is done on
individual silos which creates
data gaps in analysis
increasing errors in output
8. # Data Collection – Plethora of Devices
On a macro scale, all patient data can be captured through medical devices such as
blood glucose monitors, mobile apps and wearables, or through nano tracking and
data gleaned from cell research
We tag data from every
digital touchpoint and
maintain data by a single
patient. Tag integration is
done in collaboration with
the device manufacturer
9. # Data Tagging and Unification
Every bit of input data is tagged to its individual patient using the semantic graph.
This creates a unified stack of all data parameters specific to the individual patient.
This stack is used for comparison with the generic constants in the compute stack
for any possible deviations
This is the core part. Data is
unified by patient. The stack
is compared and the
deviations are collected to
deduce possible scenarios
10. # Personalized Medicine - Unified Data of Single Patient
The created unified stack has all the required ‘ingredients’ to help customise and
deliver bespoke drugs or therapies, thereby providing the infrastructure platform for
personalised care
11. # Faster Diagnosis
The Machine Learning algorithms can arrive at the most probable scenario or even
request for extra data parameters in addition to all the initially provided laboratory
inputs, to arrive at an accurate decision in minutes.
The possible scenarios is
parsed through an
probabilistic attribution
model to arrive at most
possible outcome
12. # Preventive Care – Predictions and Prescriptions
The holistic analysis allows the machine to visualize conditions beyond the
symptom area and help predict unseen variances or differences. Data collected
periodically from home devices allow in predicting the possibility of future
scenarios and come up with remedial strategies to negate it
The possible outcome is
predicted for downstream
effects and the most
effective pathway is
prescribed for faster repair
13. # Remedies and Solutions
Remedies and Solutions involve all repair strategies that require administration
through either Diet, Drugs or targeted Nano medicine. This resultant particular
data set can be integrated with pharmaceutical company products and help to
deliver personalised drugs, or to a Nano lab which will help to deliver a critical
enzyme to a specific in-vivo target site
The repair is either
prescribed as a simple diet
routine or through drugs or
through a surgery and in the
future, through nano devices
14. # Solution Effectiveness Monitoring
Monitoring of the in-vivo effectiveness of the delivered repair strategy is essential
for effectiveness. Data is tracked through devices and parsed into the analysis
cycle to help catalyse further processes or to understand the effectiveness of the
delivered repair strategy
When the prescription is
administered, it becomes
necessary to monitor the
effects. In case, the
prescription is not working
out, other suggested
therapies can be
administered without
wasting time
15. # Learning and Innovations
All data so collected from Repair Strategies help to provide critical insight into
newer patterns and thus innovate for further effectiveness of the solution. This
will help to further exploration in research, drug discovery, signalling pathway
manipulations along with myriad other techniques
Every output from repair
strategies helps in learning.
Machine learning algorithms
can detect new patterns or
insights and learn quicker
resolution patterns from this
data. This gives speed to cell
research activities
16. # Insights from Unification
Data ‘Unification’ in itself will reveal more secrets to further our understanding of
human biology. Eg; Unification of all MRI data, PET data and EEG data will help
expose a higher understanding of functioning of the human brain
Unifying data from 2
different sources can reveal a
lot of patterns allowing
machine learning module to
pick up complete patterns.
17. # Machine Learning and Robotic Assistance
The network allows in learning behaviour of a chemical structure by attributing
every corresponding unit parameter and mapping patterns of any downstream
influences of that chemical structure. This allows the machine to learn and make
decisions right at the molecular level and arrive at finer outputs
Machine learning
to arrive at
accurate range
scale
Machine learning
on source data for
patterns related to
subject area
Learning on
unified single
patient data to
understand
individual patterns
Learning to
understand the
patterns
associated with
repair
18. The Quahog Decision platform can change the way health care is
managed.
● Allow for the patient/ individual to be more aware
● Assist doctors to make the right decision quick and monitor the
effectiveness of that decision
● Allow the many researchers across healthcare to solve the many
problems through a holistic approach
● Helping current cellular researchers work on ‘unified’ cell
parameters, giving better outputs
● Output exacting parameters to allow for personalised drug printing
With this projected accuracy and speed, we will not only save many
otherwise unfortunate lives, but also be able to explore newer paths
such as cellular rejuvenation and energy restoration in delaying the
various problems associated with biologic ageing