Abstract: Now a days detection of patients with elevated risk of diabetes mellitus is developing critical to the improved prevention and overall health management of these patients. We aim to apply association rule mining to electronic medical records (EMR) to invent sets of risk factors and their corresponding subpopulations that represent patients which have high risk of developing diabetes. With the high linearity of EMRs, association rule mining generates a very large set of rules which we need to summarize for easy medical use. We reviewed four association rule set summarization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, advantages and drawbacks. We proposed extensions to incorporate risk of diabetes into the process of finding an optimum summary. We evaluated these modified techniques on a real-world border line diabetes patient associate. We found that all four methods gives summaries that described subpopulations at high risk of diabetes with every method having its clear strength. In this extension to the Bottom-Up Summarization (BUS) algorithm produced the most suitable summary. The subpopulations identified by this summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.
Keywords: Agile model, Association rules, Association rule summarization, Data mining, Survival analysis, Fuzzy Clustering.
Title: Diabetes Mellitus Prediction System Using Data Mining
Author: Yamini Amrale, Arti Shedge, Sonal Singh, Anjum Shaikh
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
Performance Analysis of Data Mining Methods for Sexually Transmitted Disease ...IJECEIAES
According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.
Mining Health Examination Records A Graph Based Approachijtsrd
EHR Electronic Health Records collects data on yearly basis and it is used in many countries for healthcare.HER Health Examination Records collects the data on regular basis and identifies the participants at risk that is important for early warning and prevention.the fundamental challenge is for learning classification model for risk prediction with unlabelled data and live data string that established the majority of the collected dataset.the unlabelled data string describes the participants in health examintions whose health conditions can be vary from healthy to highly risky or very ill.in this paper, we propose a graph based,semisupervised learning algorithm called SHG health semi supervised heterogenous graph on Health for risk prediction and assessment to classify a progressively developing condition with the majority of the data unlabelled. An efficient iterative algorithm is designed and developed to proof the convergence is given.extensive experiments based on both real health examination dataset and live datasets to show effectiveness of our method. Jayashri A. Sonawane | Dr. Swati A. Bhavsar ""Mining Health Examination Records - A Graph Based Approach"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22810.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/22810/mining-health-examination-records---a-graph-based-approach/jayashri-a-sonawane
The role of real world data and evidence in building a sustainable & efficien...Office of Health Economics
This presentation defines RWD and RWE in the context of digital health, and looks at potential uses for RWD and RWE. It briefly sets out the current landscape in Malaysia and looks at the challenges in using RWE. In particular, the issues of access, governance and ensuring good quality are considered.
Performance Analysis of Data Mining Methods for Sexually Transmitted Disease ...IJECEIAES
According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.
Mining Health Examination Records A Graph Based Approachijtsrd
EHR Electronic Health Records collects data on yearly basis and it is used in many countries for healthcare.HER Health Examination Records collects the data on regular basis and identifies the participants at risk that is important for early warning and prevention.the fundamental challenge is for learning classification model for risk prediction with unlabelled data and live data string that established the majority of the collected dataset.the unlabelled data string describes the participants in health examintions whose health conditions can be vary from healthy to highly risky or very ill.in this paper, we propose a graph based,semisupervised learning algorithm called SHG health semi supervised heterogenous graph on Health for risk prediction and assessment to classify a progressively developing condition with the majority of the data unlabelled. An efficient iterative algorithm is designed and developed to proof the convergence is given.extensive experiments based on both real health examination dataset and live datasets to show effectiveness of our method. Jayashri A. Sonawane | Dr. Swati A. Bhavsar ""Mining Health Examination Records - A Graph Based Approach"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22810.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/22810/mining-health-examination-records---a-graph-based-approach/jayashri-a-sonawane
The role of real world data and evidence in building a sustainable & efficien...Office of Health Economics
This presentation defines RWD and RWE in the context of digital health, and looks at potential uses for RWD and RWE. It briefly sets out the current landscape in Malaysia and looks at the challenges in using RWE. In particular, the issues of access, governance and ensuring good quality are considered.
ABSTRACT
Objective: Stroke is one of the leading causes of death and disabilities worldwide. Cost-effectiveness analysis helps identify neglected opportunities
by highlighting interventions that are relatively inexpensive, yet have the potential to reduce the disease burden substantially. In India, there are
wide social and economic disparities. Socioeconomic environment influences occupation, lifestyle, and nutrition of social classes which in turn would
influence the prevalence and profile of stroke. By reduction of delays in access to hospital and improving provision of affordable treatments can
reduce morbidity and mortality in patients with stroke in India. This study is designed to measure and compare the costs (resources consumed) and
consequences (clinical, economic, and humanistic) of pharmaceutical products and services and their impact on individuals, healthcare systems and
society.
Methods: The purpose of this study is to analyze and conduct a cost-effectiveness analysis for the treatment of stroke in Guntur City Hospitals.
The patients were treated either with aspirin or clopidogrel. The health outcomes were measured using Modified Rankin Scale, A prominent risk
assessment scale for stroke. The pharmacoeconomic data were computed from the patient data collection forms.
Result: The incremental cost-effectiveness ratio of aspirin and clopidogrel were calculated to be Rs. 8046.2/year.
Conclusion: The study concludes that aspirin has the increased socioeconomic impact when compared to Clopidogrel and we can see that the earlier
therapy has supported discharge, home-based rehabilitation along with reduced hospital stay and hence preferable.
Keywords: Stroke, Pharmacoeconomics, Cost-effectiveness analysis, Aspirin, Clopidogrel, Incremental cost-effectiveness ratio.
The convergence of separate health systems has led to
a great increase in data, which some organisations are
struggling to get to grips with. Harnessing analytic tools
and sharing knowledge is the best way forward
Clinical Research Informatics Year-in-ReviewPeter Embi
Peter Embi's 2018 Clinical Research Informatics Year-in-Review. Presented as closing Keynote address at the 2018 AMIA Informatics Summit in San Francisco, CA.
March 2, 2018
Value-based health care is one of the most pressing topics in health care finance and policy today. Value-based payment structures are widely touted as critical to controlling runaway health care costs, but are often difficult for health care entities to incorporate into their existing infrastructures. Because value-based health care initiatives have bipartisan support, it is likely that these programs will continue to play a major role in both the public and private health insurance systems. As such, there is a pressing need to evaluate the implementation of these initiatives thus far and to discuss the direction that American health care financing will take in the coming years.
To explore this important issue, the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School collaborated with Ropes & Gray LLP to host a one-day conference on value-based health care. This event brought together scholars, health law practitioners, and health care entities to evaluate the impact of value-based health care on the American health care system.
For more information, visit our website at: http://petrieflom.law.harvard.edu/events/details/will-value-based-care-save-the-health-care-system
March 02, 2018
Value-based health care is one of the most pressing topics in health care finance and policy today. Value-based payment structures are widely touted as critical to controlling runaway health care costs, but are often difficult for health care entities to incorporate into their existing infrastructures. Because value-based health care initiatives have bipartisan support, it is likely that these programs will continue to play a major role in both the public and private health insurance systems. As such, there is a pressing need to evaluate the implementation of these initiatives thus far and to discuss the direction that American health care financing will take in the coming years.
To explore this important issue, the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School collaborated with Ropes & Gray LLP to host a one-day conference on value-based health care. This event brought together scholars, health law practitioners, and health care entities to evaluate the impact of value-based health care on the American health care system.
For more information, visit our website at: http://petrieflom.law.harvard.edu/events/details/will-value-based-care-save-the-health-care-system
Presenter: Marina Sirota, UCSF
Recent advances in genome typing and sequencing technologies have enabled quick generation of a vast amount of molecular data at very low cost. The mining and computational analysis of this type of data can help shape new diagnostic and therapeutic strategies in biomedicine. In this talk, I will discuss how such technological advances in combination with data science and integrative analysis can be applied to drug discovery in the context of drug target identification, computational drug repurposing, and population stratification approaches.
Development of Behavioral Risk Factor Surveillance System Management in the R...inventionjournals
The current health care system in the Republic of Moldova is not adequately focused on control and prevention of behavioral risk factors. There are no systems for systematically collecting behavioral risk factors data. The main goal of the research was to assess the feasibility of performing Behavioral Risk Factor Surveillance System (BRFSS) in the Republic of Moldova using the U.S. BRFSS standards, and provide evidence-based recommendations for new implementation. The proposed recommendations in base of performed SWOT analysis are opportunities to extend the good practice of U.S. health system in order to contribute for improvement of cardiovascular health in the Republic of Moldova.
White House Office of National Drug Control Policy on the implications of health reform in substance abuse prevention and treatment.
(Keith Humphreys
Senior Policy Advisor, White House ONDCP)
With the upcoming move to ICD-10 Procedure Codes across the world, information flow will reach many new recipients to improve the world's health conditions!
Summarization Techniques in Association Rule Data Mining For Risk Assessment ...IJTET Journal
Abstract— At Early exposure of patients with dignified risk of developing diabetes mellitus is so hyper critical to the bettered prevention and global clinical management of these patients. In an existing system, apriori algorithm is used to find the itemsets for association rules but it is not efficient in finding itemsets and it uses only four association rules for finding the risk of diabetes mellitus so it have low precision. In this paper we are focusing to implement association rule mining to electronic medical records to detect set of danger factors and their equivalent or identical subpopulations that indicates patients at especially steep risk of progressing diabetes. Association rule mining accomplishes a very bulky set of rules for summarizing the EMR with huge dimensionability. We proposed a system in enlargement to combine risk of diabetes for the purpose of finding an suitable summary for this we use ten association rule and using the reorder algorithm for finding the itemsets and rules. For identifying the risk we considered four association rule set summarization techniques and organised a related calculation to support counselling with respect to their applicability merits and demerits and provide solutions to reduce the risk of diabetes. The above four methods having its fair strength but the bus algorithm developed the best acceptable summary.
DIAGNOSIS OF OBESITY LEVEL BASED ON BAGGING ENSEMBLE CLASSIFIER AND FEATURE S...ijaia
In the current era, the amount of data generated from various device sources and business transactions is
rising exponentially, and the current machine learning techniques are not feasible for handling the massive
volume of data. Two commonly adopted schemes exist to solve such issues scaling up the data mining
algorithms and data reduction. Scaling the data mining algorithms is not the best way, but data reduction
is feasible. There are two approaches to reducing datasets selecting an optimal subset of features from the
initial dataset or eliminating those that contribute less information. Overweight and obesity are increasing
worldwide, and forecasting future overweight or obesity could help intervention. Our primary objective is
to find the optimal subset of features to diagnose obesity. This article proposes adapting a bagging
algorithm based on filter-based feature selection to improve the prediction accuracy of obesity with a
minimal number of feature subsets. We utilized several machine learning algorithms for classifying the
obesity classes and several filter feature selection methods to maximize the classifier accuracy. Based on
the results of experiments, Pairwise Consistency and Pairwise Correlation techniques are shown to be
promising tools for feature selection in respect of the quality of obtained feature subset and computation
efficiency. Analyzing the results obtained from the original and modified datasets has improved the
classification accuracy and established a relationship between obesity/overweight and common risk factors
such as weight, age, and physical activity patterns.
ABSTRACT
Objective: Stroke is one of the leading causes of death and disabilities worldwide. Cost-effectiveness analysis helps identify neglected opportunities
by highlighting interventions that are relatively inexpensive, yet have the potential to reduce the disease burden substantially. In India, there are
wide social and economic disparities. Socioeconomic environment influences occupation, lifestyle, and nutrition of social classes which in turn would
influence the prevalence and profile of stroke. By reduction of delays in access to hospital and improving provision of affordable treatments can
reduce morbidity and mortality in patients with stroke in India. This study is designed to measure and compare the costs (resources consumed) and
consequences (clinical, economic, and humanistic) of pharmaceutical products and services and their impact on individuals, healthcare systems and
society.
Methods: The purpose of this study is to analyze and conduct a cost-effectiveness analysis for the treatment of stroke in Guntur City Hospitals.
The patients were treated either with aspirin or clopidogrel. The health outcomes were measured using Modified Rankin Scale, A prominent risk
assessment scale for stroke. The pharmacoeconomic data were computed from the patient data collection forms.
Result: The incremental cost-effectiveness ratio of aspirin and clopidogrel were calculated to be Rs. 8046.2/year.
Conclusion: The study concludes that aspirin has the increased socioeconomic impact when compared to Clopidogrel and we can see that the earlier
therapy has supported discharge, home-based rehabilitation along with reduced hospital stay and hence preferable.
Keywords: Stroke, Pharmacoeconomics, Cost-effectiveness analysis, Aspirin, Clopidogrel, Incremental cost-effectiveness ratio.
The convergence of separate health systems has led to
a great increase in data, which some organisations are
struggling to get to grips with. Harnessing analytic tools
and sharing knowledge is the best way forward
Clinical Research Informatics Year-in-ReviewPeter Embi
Peter Embi's 2018 Clinical Research Informatics Year-in-Review. Presented as closing Keynote address at the 2018 AMIA Informatics Summit in San Francisco, CA.
March 2, 2018
Value-based health care is one of the most pressing topics in health care finance and policy today. Value-based payment structures are widely touted as critical to controlling runaway health care costs, but are often difficult for health care entities to incorporate into their existing infrastructures. Because value-based health care initiatives have bipartisan support, it is likely that these programs will continue to play a major role in both the public and private health insurance systems. As such, there is a pressing need to evaluate the implementation of these initiatives thus far and to discuss the direction that American health care financing will take in the coming years.
To explore this important issue, the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School collaborated with Ropes & Gray LLP to host a one-day conference on value-based health care. This event brought together scholars, health law practitioners, and health care entities to evaluate the impact of value-based health care on the American health care system.
For more information, visit our website at: http://petrieflom.law.harvard.edu/events/details/will-value-based-care-save-the-health-care-system
March 02, 2018
Value-based health care is one of the most pressing topics in health care finance and policy today. Value-based payment structures are widely touted as critical to controlling runaway health care costs, but are often difficult for health care entities to incorporate into their existing infrastructures. Because value-based health care initiatives have bipartisan support, it is likely that these programs will continue to play a major role in both the public and private health insurance systems. As such, there is a pressing need to evaluate the implementation of these initiatives thus far and to discuss the direction that American health care financing will take in the coming years.
To explore this important issue, the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School collaborated with Ropes & Gray LLP to host a one-day conference on value-based health care. This event brought together scholars, health law practitioners, and health care entities to evaluate the impact of value-based health care on the American health care system.
For more information, visit our website at: http://petrieflom.law.harvard.edu/events/details/will-value-based-care-save-the-health-care-system
Presenter: Marina Sirota, UCSF
Recent advances in genome typing and sequencing technologies have enabled quick generation of a vast amount of molecular data at very low cost. The mining and computational analysis of this type of data can help shape new diagnostic and therapeutic strategies in biomedicine. In this talk, I will discuss how such technological advances in combination with data science and integrative analysis can be applied to drug discovery in the context of drug target identification, computational drug repurposing, and population stratification approaches.
Development of Behavioral Risk Factor Surveillance System Management in the R...inventionjournals
The current health care system in the Republic of Moldova is not adequately focused on control and prevention of behavioral risk factors. There are no systems for systematically collecting behavioral risk factors data. The main goal of the research was to assess the feasibility of performing Behavioral Risk Factor Surveillance System (BRFSS) in the Republic of Moldova using the U.S. BRFSS standards, and provide evidence-based recommendations for new implementation. The proposed recommendations in base of performed SWOT analysis are opportunities to extend the good practice of U.S. health system in order to contribute for improvement of cardiovascular health in the Republic of Moldova.
White House Office of National Drug Control Policy on the implications of health reform in substance abuse prevention and treatment.
(Keith Humphreys
Senior Policy Advisor, White House ONDCP)
With the upcoming move to ICD-10 Procedure Codes across the world, information flow will reach many new recipients to improve the world's health conditions!
Summarization Techniques in Association Rule Data Mining For Risk Assessment ...IJTET Journal
Abstract— At Early exposure of patients with dignified risk of developing diabetes mellitus is so hyper critical to the bettered prevention and global clinical management of these patients. In an existing system, apriori algorithm is used to find the itemsets for association rules but it is not efficient in finding itemsets and it uses only four association rules for finding the risk of diabetes mellitus so it have low precision. In this paper we are focusing to implement association rule mining to electronic medical records to detect set of danger factors and their equivalent or identical subpopulations that indicates patients at especially steep risk of progressing diabetes. Association rule mining accomplishes a very bulky set of rules for summarizing the EMR with huge dimensionability. We proposed a system in enlargement to combine risk of diabetes for the purpose of finding an suitable summary for this we use ten association rule and using the reorder algorithm for finding the itemsets and rules. For identifying the risk we considered four association rule set summarization techniques and organised a related calculation to support counselling with respect to their applicability merits and demerits and provide solutions to reduce the risk of diabetes. The above four methods having its fair strength but the bus algorithm developed the best acceptable summary.
DIAGNOSIS OF OBESITY LEVEL BASED ON BAGGING ENSEMBLE CLASSIFIER AND FEATURE S...ijaia
In the current era, the amount of data generated from various device sources and business transactions is
rising exponentially, and the current machine learning techniques are not feasible for handling the massive
volume of data. Two commonly adopted schemes exist to solve such issues scaling up the data mining
algorithms and data reduction. Scaling the data mining algorithms is not the best way, but data reduction
is feasible. There are two approaches to reducing datasets selecting an optimal subset of features from the
initial dataset or eliminating those that contribute less information. Overweight and obesity are increasing
worldwide, and forecasting future overweight or obesity could help intervention. Our primary objective is
to find the optimal subset of features to diagnose obesity. This article proposes adapting a bagging
algorithm based on filter-based feature selection to improve the prediction accuracy of obesity with a
minimal number of feature subsets. We utilized several machine learning algorithms for classifying the
obesity classes and several filter feature selection methods to maximize the classifier accuracy. Based on
the results of experiments, Pairwise Consistency and Pairwise Correlation techniques are shown to be
promising tools for feature selection in respect of the quality of obtained feature subset and computation
efficiency. Analyzing the results obtained from the original and modified datasets has improved the
classification accuracy and established a relationship between obesity/overweight and common risk factors
such as weight, age, and physical activity patterns.
Machine learning approach for predicting heart and diabetes diseases using da...IAESIJAI
Environmental changes and food habits affect people's health with numerous diseases in today's life. Machine learning is a technique that plays a vital role in predicting diseases from collected data. The health sector has plenty of electronic medical data, which helps this technique to diagnose various diseases quickly and accurately. There has been an improvement in accuracy in medical data analysis as data continues to grow in the medical field. Doctors may have a hard time predicting symptoms accurately. This proposed work utilized Kaggle data to predict and diagnose heart and diabetic diseases. The diseases heart and diabetes are the foremost cause of higher death rates for people. The dataset contains target features for the diagnosis of heart disease. This work finds the target variable for diabetic disease by comparing the patient's blood sugars to normal levels. Blood pressure, body mass index (BMI), and other factors diagnose these diseases and disorders. This work justifies the filter method and principal component analysis for selecting and extracting the feature. The main aim of this work is to highlight the implementation of three ensemble techniques-Adaptive boost, Extreme Gradient boosting, and Gradient boosting-as well as the emphasis placed on the accuracy of the results.
Great article on how to integrate machine learning and optimization technique.
One group of researchers was able to reduce heart failure readmissions by 35% by combining machine learning and decision science technique, see "Data-driven decisions for reducing readmissions for heart failure: general methodology and case study" (Bayati, et. al., 2014).
Patient Data Collection Methods. Retrospective Insights.QUESTJOURNAL
Introduction: Multiple classic and modern data collection techniques are presented in the current paper, but only a mix of them provides the appropriate approach to address patient safety problems. The current study aims to reveal the data collection methods applied worldwide. Materials and Methods: All scientific sources of the current article were identified mainly by research on Internet. The matching words used in the search of materials are “data collection methods”, “hospital reporting systems”, “incident reporting systems”, “patient events”, “patient reported data”. Relevant articles and studies covering the 2003-2016 timeframe were selected as a reference. Results: Various data collection procedures are available worldwide. During several years of research, it was concluded that a significant number of patient studies use the following patient data collection methods: retrospective record review, record review of current inpatients, staff interview of current inpatients and nominal group technique based consensus method. Conclusion: New trends in data collection techniques are also discussed, as they reveal the potential of the electronic environment. Future insights on this topic should consider the standardization of different data collection methods in order to improve data comparability aspects.
Diabetes Prediction by Supervised and Unsupervised Approaches with Feature Se...IJARIIT
Two approaches to building models for prediction of the onset of Type diabetes mellitus in juvenile subjects were examined. A set of tests performed immediately before diagnosis was used to build classifiers to predict whether the subject would be diagnosed with juvenile diabetes. A modified training set consisting of differences between test results taken at different times was also used to build classifiers to predict whether a subject would be diagnosed with juvenile diabetes. Supervised were compared with decision trees and unsupervised of both types of classifiers. In this study, the system and the test most likely to confirm a diagnosis based on the pre-test probability computed from the patient's information including symptoms and the results of previous tests. If the patient's disease post-test probability is higher than the treatment threshold, a diagnostic decision will be made, and vice versa. Otherwise, the patient needs more tests to help make a decision. The system will then recommend the next optimal test and repeat the same process. In this thesis find out which approach is better on diabetes dataset in weka framework. Also use feature selection techniques which reduce the features and complexities of process
Machine learning and operations research to find diabetics at risk for readmisison.
A team of researchers was able to apply machine learning to reduce readmissions for diabetics, see "Identifying diabetic patients with high risk of readmission" (Bhuvan,Kumar, Zafar, Aand Kishore, 2016).
EDM ForumEDM Forum CommunityeGEMs (Generating Evidence & M.docxgreg1eden90113
EDM Forum
EDM Forum Community
eGEMs (Generating Evidence & Methods to
improve patient outcomes) Publish
4-20-2017
Reducing Healthcare Costs Through Patient
Targeting: Risk Adjustment Modeling to Predict
Patients Remaining High-Cost
Jonathan A. Wrathall
Intermountain Healthcare, [email protected]
Tom Belnap
Intermountain Healthcare, [email protected]
Follow this and additional works at: http://repository.edm-forum.org/egems
Part of the Other Medicine and Health Sciences Commons, and the Social Statistics Commons
This Methods Case Study is brought to you for free and open access by the the Publish at EDM Forum Community. It has been peer-reviewed and
accepted for publication in eGEMs (Generating Evidence & Methods to improve patient outcomes).
The Electronic Data Methods (EDM) Forum is supported by the Agency for Healthcare Research and Quality (AHRQ), Grant 1U18HS022789-01.
eGEMs publications do not reflect the official views of AHRQ or the United States Department of Health and Human Services.
Recommended Citation
Wrathall, Jonathan A. and Belnap, Tom (2017) "Reducing Healthcare Costs Through Patient Targeting: Risk Adjustment Modeling to
Predict Patients Remaining High-Cost," eGEMs (Generating Evidence & Methods to improve patient outcomes): Vol. 5: Iss. 2, Article 4.
DOI: https://doi.org/10.13063/2327-9214.1279
Available at: http://repository.edm-forum.org/egems/vol5/iss2/4
Reducing Healthcare Costs Through Patient Targeting: Risk Adjustment
Modeling to Predict Patients Remaining High-Cost
Abstract
Context: The transition to population health management has changed the healthcare landscape to identify
high risk, high cost patients. Various measures of patient risk have attempted to identify likely candidates for
care management programs. Pre-screening patients for outreach has often required several years of data.
Intermountain Healthcare relied on cost-ranking algorithms which had limited predictive ability. A new risk-
adjusted algorithm shows improvements in predicting patients’ future cost status to facilitate identifying
patient eligibility for care management.
Case Description: A retrospective cohort study design was used to evaluate high-cost patient status for two
of the next three years. Modeling was developed using logistic regression and tested against other decision tree
methods. Key variables included those readily available in electronic health records supplemented by
additional clinical data and estimates of socio-economic status.
Findings: The risk-adjusted modeling correctly identified 79.0% of patients ranking among the top 15% of
costs in one of the next three years. In addition, it correctly estimated 48.1% of the patients in the top 15% cost
group in two of the next three years. This method identified patients with higher medical costs and more
comorbid conditions than previous cost-ranking methods.
Major Themes: This approach improves the predictive accuracy of identifying high cost patients in the future
.
Theory and Practice of Integrating Machine Learning and Conventional Statisti...University of Malaya
The practice of medical decision making is changing rapidly with the development of innovative
computing technologies. The growing interest of data analysis in line with the advancement in data
science raises the question of whether machine learning can be integrated with conventional statistics
in health research. To help address this knowledge gap, this talk focuses on the conceptual
integration between conventional statistics and machine learning, with a direction towards health
research. The similarities and differences between the two are compared using mathematical
concepts and algorithms. The comparison between conventional statistics and machine learning
methods indicates that conventional statistics are the fundamental basis of machine learning, where
the black box algorithms are derived from basic mathematics, but are advanced in terms of
automated analysis, handling big data and providing interactive visualizations. While the nature of
both these methods are different, they are conceptually similar. The evidence produced here
concludes that conventional statistics and machine learning are best to be integrated to develop
automated data analysis tools. Health researchers may explore machine learning as a potential tool to
enhance conventional statistics in data analytics for added reliable validation measures.
A KNOWLEDGE DISCOVERY APPROACH FOR BREAST CANCER MANAGEMENT IN THE KINGDOM OF...hiij
In this paper, we introduce an approach to improve and support decision-making process for breast cancer management in the Kingdom of Saudi Arabia. This can be accomplished by applying different association rule mining algorithms on the cancer information system in Saudi Arabia. It also provides valuable information about predicted distribution and segmentation of cancer in Saudi Arabia, which may be linked to possible risk factors. From the extracted patterns, the information need to be considered in the decision making process can be identified and recognized as well, which yields to knowledge based decisions. Consequently, identifying health risk behaviors among target group of patients and adopting intervention and preventive measures can be initiated in order to decrease breast cancer incidence and prevalence and ultimately the health care costs.
A KNOWLEDGE DISCOVERY APPROACH FOR BREAST CANCER MANAGEMENT IN THE KINGDOM OF...hiij
In this paper, we introduce an approach to improve and support decision-making process for breast cancer management in the Kingdom of Saudi Arabia. This can be accomplished by applying different association rule mining algorithms on the cancer information system in Saudi Arabia. It also provides valuable information about predicted distribution and segmentation of cancer in Saudi Arabia, which may be linked to possible risk factors. From the extracted patterns, the information need to be considered in the decision making process can be identified and recognized as well, which yields to knowledge based decisions.Consequently, identifying health risk behaviors among target group of patients and adopting interventional and preventive measures can be initiated in order to decrease breast cancer incidence and prevalence and ultimately the health care costs
Big data approaches to healthcare systemsShubham Jain
The idea behind this presentation is to explore how big data will revolutionize existing healthcare system effectively by reducing healthcare concerns such as the selection of appropriate treatment paths, quality of healthcare systems and so on. Large amount of unstructured data is available in various organizations (payers, providers, pharmaceuticals). We will discuss all the intricacies involved in massive datasets of healthcare systems and how combination of VPH technologies and big data resulted into some mind-boggling consequences. Major opportunities in healthcare includes the integration of various data pools such as clinical data, pharmaceutical R&D data and patient behaviour and sentiment data. Finding potential insights from big data with the help of medical image processing techniques, predictive modelling etc. will eventually help us to leverage the ever-increasing costs of care, help providers practice more effective medicine, empower patients and caregivers, support fitness and preventive self-care, and to dream about more personalized medicine.
Clinical data science is a rapidly evolving field that utilizes advanced analytics and machine learning techniques to extract meaningful insights from large scale healthcare data. In recent years, there has been a significant increase in the availability of electronic health records, genomic data, wearable devices, and other digital health technologies, generating vast amounts of data. This article presents a comprehensive review of the current state of clinical data science and its future prospects. The review begins by providing an overview of the foundational concepts and methodologies employed in clinical data science. It explores various data sources, including structured and unstructured data, and highlights the challenges associated with data quality, privacy, and interoperability. The role of artificial intelligence and machine learning algorithms in data analysis and prediction is examined, along with the importance of data preprocessing and feature selection techniques. G. Dileepkumar | Nimisha Prajapati | Simhavalli Godavarthi "Clinical Data Science and its Future" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd58588.pdf Paper URL: https://www.ijtsrd.com.com/pharmacy/pharmacy-practice/58588/clinical-data-science-and-its-future/g-dileepkumar
PREDICTION OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUESIAEME Publication
Diabetes mellitus is a common disease caused by a set of metabolic ailments
where the sugar stages over drawn-out period is very high. It touches diverse organs
of the human body which therefore harm a huge number of the body's system, in
precise the blood strains and nerves. Early prediction in such disease can be exact
and save human life. To achieve the goal, this research work mainly discovers
numerous factors associated to this disease using machine learning techniques.
Machine learning methods provide effectual outcome to extract knowledge by building
predicting models from diagnostic medical datasets together from the diabetic
patients. Quarrying knowledge from such data can be valuable to predict diabetic
patients. In this research, six popular used machine learning techniques, namely
Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), C4.5 Decision
Tree (DT), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) are
compared in order to get outstanding machine learning techniques to forecast diabetic
mellitus. Our new outcome shows that Support Vector Machine (SVM) achieved
higher accuracy compared to other machine learning techniques.
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
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A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
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Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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Diabetes Mellitus Prediction System Using Data Mining
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International Journal of Recent Research in Mathematics Computer Science and Information Technology
Vol. 4, Issue 1, pp: (37-41), Month: April 2017 – September 2017, Available at: www.paperpublications.org
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Paper Publications
Diabetes Mellitus Prediction System Using Data
Mining
Yamini Amrale1
, Arti Shedge2
, Sonal Singh3
, Anjum Shaikh4
Savitribai Pune University
Abstract: Now a days detection of patients with elevated risk of diabetes mellitus is developing critical to the
improved prevention and overall health management of these patients. We aim to apply association rule mining to
electronic medical records (EMR) to invent sets of risk factors and their corresponding subpopulations that
represent patients which have high risk of developing diabetes. With the high linearity of EMRs, association rule
mining generates a very large set of rules which we need to summarize for easy medical use. We reviewed four
association rule set summarization techniques and conducted a comparative evaluation to provide guidance
regarding their applicability, advantages and drawbacks. We proposed extensions to incorporate risk of diabetes
into the process of finding an optimum summary. We evaluated these modified techniques on a real-world border
line diabetes patient associate. We found that all four methods gives summaries that described subpopulations at
high risk of diabetes with every method having its clear strength. In this extension to the Bottom-Up
Summarization (BUS) algorithm produced the most suitable summary. The subpopulations identified by this
summary covered most high-risk patients, had low overlap and were at very high risk of diabetes.
Keywords: Agile model, Association rules, Association rule summarization, Data mining, Survival analysis ,Fuzzy
Clustering.
I. INTRODUCTION
Diabetes mellitus may be a increasing outbreak that affects 25.8 million individuals within the U.S. (8% of population),
and just about seven million of them don't hold they have the sickness polygenic disease results in vital medical problems
as well as anaemia heart disease ,heart condition , cardiopathie, cardiovascular sickness, stroke, renal disorder,
retinopathy, pathology and peripheral vascular disease[2]. Early prediction of patients at risk of increasing polygenic
disease may be a major health care demand. Proper management of patients in danger with manner changes and or
medications will decrease the chance of developing diabetes by upcoming future. Multiple risk factors have been known
touching an out sized rate of the population. as an example, pre diabetes (blood sugar levels above traditional ratio
however below the extent of criteria for diabetes) is gift in just about thirty fifth of a adult population and will increase
absolutely the risk of polygenic disease three to tenfold counting on the presence of further associated risk factors, like
avoirdupois, idiopathic, hyperlipidemia etc. Association rules area unit implications that associate a group of potentially
interacting cons (e.g. BMI and therefore the presence of cardiovascular disease diagnosis) with elevated risk. The use of
association rules is predominantly worthwhile, because in addition to quantifying the polygenic disorder risk, they
conjointly promptly supply the MD with a rationale, namely the associated set of conditions. This set of conditions is used
to give proper guidance treatment towards a additional customized and targeted preventive care or polygenic disorder
management. A number of winning association rule set report techniques are planned however no clear steering exists
concerning the concernment, strengths and weaknesses of those techniques. the main target of this palimpsest is to review
and characterize four existing association rule report techniques and supply steering to practitioners in selecting the for
most appropriate one. A common defect of those techniques is their inability to take polygenic disorder risk–a progressive
2. ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Vol. 4, Issue 1, pp: (37-41), Month: April 2017 – September 2017, Available at: www.paperpublications.org
Page | 38
Paper Publications
outcome–into account. In order to form these a lot of applicable, we had to minimize the change we incline to expand
them to include information regarding progressive outcome variables.
II. LITERATURE SURVEY
1]A polygenic disease index is in essence a prophetic model that assigns a score to a patient supported his calculable risk
of polygenic disease. Collins conducted an intensive survey of polygenic disease indices describes the dangerous factors
and also the modeling technique that these evidence used.
2] They found that most indices were characterized by in nature and none of the surveyed indices have taken
communications among the adverse factors into consideration. While we have a tendency to don't seem to be awake to
any new polygenic disease index disclose after the survey, a current study specializing in the metabolic syndrome (of that
polygenic disease may be a component) represents a big progress .
3] Used association rule mining to continuosly explore incident of guessable codes. The ensuring association rules dont
constitute a polygenic disease index as a result of the study doesn’t designate a specific outcome of interest and that they
dont evaluate or predict the adverse of polygenic disease in patients, but they Invented some important associations
between predictable codes.
4]We have at present a polygenic disease study wherever we target to get the relationships among diseases in the
metabolic syndrome. We have a tendency to used identical outfit as this current study, however, we have a Aim to
enclosed solely eight identification codes and age as predictors.
5]We invent association rules by considering a number of the eight belief codes, assessed the risk of polygenic disease
that the rules confer on patients and presented the principles as a increasable graph described however patients towards
from a healthy state to polygenic disease.
6]We indisputable that a approach found clinical meaningful association rules that square measure in step with our
medical expectation. With simply eight predictor variables, the dimensions of the invented rule set was moderate thirteen
important rules– and outcome, disturbance was simplest. Naturally, there is no rule-set account was mandatory.
III. TECHNOLOGIES
Data mining:
Data mining is the analysis step of Knowledge Discovery in Databases or KDD. It is an interdisciplinary subfield of
computer science. This is the process of discovering patterns in large datasets ("big data") involving methods at the
intersection of artificial intelligence, machine learning, database systems and statistics. The final goal of the data mining
process is to extract relevant information from a dataset and transform it into an understandable format for further use.
Apriori Hybrid Algorithm
Apriori and Apriori Transaction ID algorithms can be combined into a hybrid algorithm, called Apriori Hybrid. It scales
linearly with the number of transactions. In addition, the execution time reduce a little as the number of items in the
database increases. As the average transaction size increases (while keeping the database size constant), the execution
time increases only gradually. These experiments demonstrate the feasibility of using Apriori Hybrid in real applications
involving very large databases.
Association rule summarization techniques
We apply rule for data set summarization techniques namely APRX-Collection, RP Global, BUS to evaluate the Risk of
Diabetes mellitus. Predicition of Diabetes mellitus depends on Body condition, Tablets .Morbidity of the Observed
patients in dataset subpopulation.
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IV. ARCHITECTURE
System architecture is described below:
Figure 1. Architecture
This system consists of two datasets. First is big dataset and the other one is upload dataset.
Initially this application there is no Database Records. Summarization techniques are implemented in a Distributed
Database not only in a single database.So have to ask permission to access the database of every medical Center
Administrator .
Discover Item sets and association rule:
Finding of association rules by using the apriori hybrid algorithm. The apriori hybrid algorithm, a variant of the well-
known Apriori algorithm that discovers candidate set of items that contain specific items the item corresponding to the
diabetes prediction results at final in this case.
Un summarized rule for data:
It consists of the simillar risk and high risk of Observe patients in datasets. These values are calculated depends on the
sugar level, BP, BMI, Tablets etc.
Apply summarization techniques:
Rule for data set summarization techniques is used to evaluate the Risk of Diabetes mellitus. Predicition of Diabetes
mellitus depends on Body condition, Tablets and Co., Morbites of the Observed patients in dataset subpopulation.
V. RESULT
The proposed technique intend to analyze the risk of diabetes mellitus. Here four association rule are used .Summarization
techniques such as APRX-COLLECTION, RP Global, Top K and BUS. All these methods have its own advantage but
BUS algorithm is the most efficient.
1. Age in Year.
2. Sex- (value 1: Male; value 0: Female).
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3. Thal - (value 3: normal; value 6: fixed defect; value 7: reversible defect).
4. CA – number of major vessels colored by fluoroscopy (value 0-3).
5. Old peak – ST depression induced by exercise.
6. Exang - exercise induced angina (value 1: yes; value 0: no).
7. Chest Pain Type -(value 1:typical type1 angina, value 2: typical type 2 angina, value 3:non-angina pain; value 4:
asymptomatic).
8. Restecg – resting electrographic results (value 0: normal; value 1: having ST-T wave abnormality; value 2: showing
probable or definite left ventricular hypertrophy).
9. Serum Cholestrol (mg/dl).
10 .Fasting Blood Sugar- (value 0: <120 mg/dl:value 1: >120 mg/dl; ).
11. Trest Blood Pressure (mm Hg on admission to the hospital).
12. Slope – the slope of the peak exercise ST segment (value 1: unsloping; value 2: flat; value 3: down sloping).
13. Thalach – maximum heart rate achieved.
14.Diabetes Disease Present - 0:No 1: Yes.
Figure 2. Relative Risk Count of Patient
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International Journal of Recent Research in Mathematics Computer Science and Information Technology
Vol. 4, Issue 1, pp: (37-41), Month: April 2017 – September 2017, Available at: www.paperpublications.org
Page | 41
Paper Publications
VI. CONCLUSION
Here we have studied diabetes mellitus prediction system using data mining solution. And association rule mining to
identify sets of risk factors and the corresponding patient subpopulations that significantly increased risk of diabetes. And
many number of association rules were discovered including the clinical interpretation results. For this method, the
number of rules are used for health interpretation makes feasible.
REFERENCES
[1] F. Afrati, A. Gionis, and H. Mannila, “Approximating a collection of frequent sets,” in Proc. ACM Int. Conf. KDD,
Washington, DC, USA, 2004.
[2] “Fast algorithms for mining association rules,” R. Agrawal and R. Srikant, in Proc. 20th VLDB, Santiago, Chile,
1994.
[3] “A statistical theory for quantitative association rules,” Y. Aumann and Y. Lindell, in Proc. 5th KDD, New York,
NY, USA, 1999.
[4] “Use of association rule mining to assess diabetes risk in patients with impared fasting glucose,” P. J. Caraballo, M.
R. Castro, S. S. Cha, P. W. Li, and G. J. Simon, in Proc. AMIA Annu. Symp., 2011.
[5] A Fuzzy Rule-Based Clustering Algorithm,proc IEEE transaction Eghbal G.Mansoori, ―FRBC:. Fuzzy sytems. Vol
19no.5, October 2011.
[6] “Mining association rules with item constraints,” R. Srikant, Q. Vu, and R. Agrawal, in Proc. AAAI, 1997.
[7] Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle
intervention or metformin. The New England Journal of Medicine, 346(6), 2002.
[8] In SIAM International Conference on Data Mining Xiaoxin Yin and Jiawei Han. CPAR: Classification based on
predictive association rules (SDM), 2003.