This document discusses using artificial intelligence (AI) to help address challenges in anomalous aortic origin of coronary artery (AAOCA). AAOCA is a leading cause of sudden cardiac death in young athletes. There are knowledge gaps in risk stratification for AAOCA patients. The document proposes a two-step approach using AI: 1) unsupervised machine learning to uncover unknown high-risk phenotypes from clinical data, and 2) supervised learning to develop refined risk stratification models. Challenges include data availability and expertise in machine learning. Future directions include increased data collaboration and human-AI partnerships to advance precision cardiovascular medicine.
IRJET - An Effective Stroke Prediction System using Predictive ModelsIRJET Journal
This document summarizes research on developing an effective stroke prediction system using predictive models. The researchers used patient characteristics data to conduct exploratory data analysis and feature selection to determine the most influential variables for predicting stroke. They then performed predictive modeling with classification algorithms like random forest, decision tree, logistic regression and support vector machines. The most accurate model was selected to develop a web application that allows users to input their information and predict their risk of having a stroke.
This document summarizes different methods for predicting stroke risk using a patient's historical medical information. It discusses scoring metrics like CHADS2 that evaluate risk factors such as heart failure, hypertension, age, and previous strokes. Bayesian Rule Lists are proposed to generate rules to predict stroke risk using decision lists. Recurrent neural networks can also be used with a custom loss function to model medical data over time. The document outlines the types of patient data needed, how to handle missing values and embed records, and how to generate and validate rules from the modeled and fitted data to predict stroke risk.
In this talk, Hector will cover some recent advances in applying machine learning to the field of healthcare. A brief overview of deep learning and its applications in healthcare such as diagnostics, care management, decision support and personalized medicine. There will be deeper dives into specific topics such as machine learning on electronic health records and analyzing EEGs.
AI for Precision Medicine (Pragmatic preclinical data science)Paul Agapow
This document summarizes a presentation on using data science approaches like machine learning for precision medicine and biomedical research. It notes that biomedical data sets are often small, which limits the use of deep learning techniques that require large amounts of labeled data. It advocates combining multiple smaller datasets together using standards to create larger datasets for analysis. It also emphasizes using multiple data types (e.g. omics data, electronic health records, social media) together through integrated analysis to provide more context than any single data type alone. It provides examples of applying these approaches to problems like classifying texts for systematic reviews and discovering asthma subtypes through multi-omics analysis.
Prediction, Big Data, and AI: Steyerberg, Basel Nov 1, 2019Ewout Steyerberg
Title"Clinical prediction models in the age of artificial intelligence and big data", presented at the Basel Biometrics Society seminar Nov 1, 2019, Basel, by Ewout Steyerberg, with substantial inout from Maarten van Smeden and Ben van Calster
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
The document discusses missing data in clinical trials and recommends Bayesian methods for imputation. It presents the objectives of comparing the performance of last observation carried forward (LOCF), baseline observation carried forward (BOCF) and multiple imputation through a Bayesian Markov Chain Monte Carlo (MCMC) method on a simulated clinical trial dataset. The Bayesian MCMC approach is applied in SAS to impute missing vitreous haze scores across multiple visits in two treatment groups using a monotone model followed by a regression model, and results in estimates of efficacy endpoints with associated statistics and confidence intervals.
IRJET - An Effective Stroke Prediction System using Predictive ModelsIRJET Journal
This document summarizes research on developing an effective stroke prediction system using predictive models. The researchers used patient characteristics data to conduct exploratory data analysis and feature selection to determine the most influential variables for predicting stroke. They then performed predictive modeling with classification algorithms like random forest, decision tree, logistic regression and support vector machines. The most accurate model was selected to develop a web application that allows users to input their information and predict their risk of having a stroke.
This document summarizes different methods for predicting stroke risk using a patient's historical medical information. It discusses scoring metrics like CHADS2 that evaluate risk factors such as heart failure, hypertension, age, and previous strokes. Bayesian Rule Lists are proposed to generate rules to predict stroke risk using decision lists. Recurrent neural networks can also be used with a custom loss function to model medical data over time. The document outlines the types of patient data needed, how to handle missing values and embed records, and how to generate and validate rules from the modeled and fitted data to predict stroke risk.
In this talk, Hector will cover some recent advances in applying machine learning to the field of healthcare. A brief overview of deep learning and its applications in healthcare such as diagnostics, care management, decision support and personalized medicine. There will be deeper dives into specific topics such as machine learning on electronic health records and analyzing EEGs.
AI for Precision Medicine (Pragmatic preclinical data science)Paul Agapow
This document summarizes a presentation on using data science approaches like machine learning for precision medicine and biomedical research. It notes that biomedical data sets are often small, which limits the use of deep learning techniques that require large amounts of labeled data. It advocates combining multiple smaller datasets together using standards to create larger datasets for analysis. It also emphasizes using multiple data types (e.g. omics data, electronic health records, social media) together through integrated analysis to provide more context than any single data type alone. It provides examples of applying these approaches to problems like classifying texts for systematic reviews and discovering asthma subtypes through multi-omics analysis.
Prediction, Big Data, and AI: Steyerberg, Basel Nov 1, 2019Ewout Steyerberg
Title"Clinical prediction models in the age of artificial intelligence and big data", presented at the Basel Biometrics Society seminar Nov 1, 2019, Basel, by Ewout Steyerberg, with substantial inout from Maarten van Smeden and Ben van Calster
Large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). Those data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment. More details are available here http://dmkd.cs.wayne.edu/TUTORIAL/Healthcare/
The document discusses missing data in clinical trials and recommends Bayesian methods for imputation. It presents the objectives of comparing the performance of last observation carried forward (LOCF), baseline observation carried forward (BOCF) and multiple imputation through a Bayesian Markov Chain Monte Carlo (MCMC) method on a simulated clinical trial dataset. The Bayesian MCMC approach is applied in SAS to impute missing vitreous haze scores across multiple visits in two treatment groups using a monotone model followed by a regression model, and results in estimates of efficacy endpoints with associated statistics and confidence intervals.
Healthcare Predicitive Analytics for Risk Profiling in Chronic Care: A Bayesi...MIS Quarterly
This document describes a study that developed a Bayesian multitask learning (BMTL) model to predict multiple chronic disease risks using electronic health record data. Specifically, the model aimed to predict risks of stroke, heart attack, and kidney failure in patients with diabetes. The study evaluated the BMTL model against single-task learning baselines and other multitask learning approaches, finding the BMTL model achieved better predictive performance. A counterfactual analysis also suggested the BMTL model could help identify more patients for preventive treatment interventions compared to current practice.
Workshop on "Building Successful Pipelines for Predictive Analytics in Healthcare" delivered by Danielle Belgrave, PhD, Researcher at Microsoft Research, Cambridge, UK.
Clinical data analytics is an exciting new area of healthcare data analytics. This presentation presents a brief overview of the topic as an introduction and whetting the curiosity of the reader.
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
Dichotomania and other challenges for the collaborating biostatisticianLaure Wynants
Conference presentation at ISCB 41 in the session
"Biostatistical inference in practice: moving beyond false
dichotomies"
A comment in Nature, signed by over 800 researchers, called for the scientific community to “retire statistical significance”. The responses included a call to halt the use of the term „statistically significant”, and changes in journal’s author guidelines. The leading discourse among statisticians is that inadequate statistical training of clinical researchers and publishing practices are to blame for the misuse of statistical testing. In this presentation, we search our collective conscience by reviewing ethical guidelines for statisticians in light of the p-value crisis, examine what this implies for us when conducting analyses in collaborative work and teaching, and whether the ATOM (accept uncertainty; be thoughtful, open and modest) principles can guide us.
Str-AI-ght to heaven? Pitfalls for clinical decision support based on AIBenVanCalster
This document summarizes some of the key pitfalls and challenges of using artificial intelligence (AI), particularly machine learning and deep learning models, for clinical decision support. It notes that (1) methodology is often poor, with small datasets and a lack of validation; (2) there is little evidence that most models actually improve outcomes; and (3) models show significant heterogeneity and may not generalize across settings and populations. It also discusses issues of proprietary datasets and models, conflicts of interest, and the challenges of actual implementation and assessing real-world impact. The document emphasizes that while AI has potential, more rigorous research is needed to develop trustworthy models that provide reliable decision support for patients and clinicians.
How to establish and evaluate clinical prediction models - StatsworkStats Statswork
A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and health education. Despite the positive effects of clinical prediction models on practice, prediction modelling is a difficult process that necessitates meticulous statistical analysis and sound clinical judgments. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following always on Time, outstanding customer support, and High-quality Subject Matter Experts.
Read More With Us: https://bit.ly/3dxn32c
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics across Methodologies | Wide Range of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
United Kingdom: 44-1143520021
India: 91-4448137070
WhatsApp: 91-8754446690
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...CSCJournals
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
This document presents a comparative study of various data mining techniques for predicting heart disease using collected and standard heart disease datasets. Five classifiers - KStar, J48, SMO, Bayes Net, and MLP - were evaluated based on their accuracy and training time. SMO had the highest accuracy of 84-89% and MLP had the lowest training time of 0.33-0.75 seconds. The techniques are also compared based on their average classification variance. The study concludes with receiver operating characteristic curves showing the performance of the techniques on the two datasets.
Development and evaluation of prediction models: pitfalls and solutions (Part...BenVanCalster
Slides for the statistics in practice session for the Biometrisches Kolloquium (organized by the Deutsche Region der Internationalen Biometrischen Gesellschaft), 16 March 2021.
Part I from Maarten van Smeden: https://www.slideshare.net/MaartenvanSmeden/development-and-evaluation-of-prediction-models-pitfalls-and-solutions
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
A Survey on Heart Disease Prediction Techniquesijtsrd
Heart disease is the main reason for a huge number of deaths in the world over the last few decades and has evolved as the most life threatening disease. The health care industry is found to be rich in information. So, there is a need to discover hidden patterns and trends in them. For this purpose, data mining techniques can be applied to extract the knowledge from the large sets of data. Many researchers, in recent times have been using several machine learning techniques for predicting the heart related diseases as it can predict the disease effectively. Even though a machine learning technique proves to be effective in assisting the decision makers, still there is a scope for developing an accurate and efficient system to diagnose and predict the heart diseases thereby helping doctors with ease of work. This paper presents a survey of various techniques used for predicting heart disease and reviews their performance. G. Niranjana | Dr I. Elizabeth Shanthi "A Survey on Heart Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38349.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38349/a-survey-on-heart-disease-prediction-techniques/g-niranjana
Open science LMU session contribution E Steyerberg 2jul20Ewout Steyerberg
1. The document discusses open science approaches to addressing big research questions through multiple analysts studying the same dataset and research question, as well as data and analysis sharing initiatives.
2. It describes challenges in open science including variation in analyses and interpretations due to heterogeneity across datasets and studies.
3. Initiatives like OHDSI are highlighted as bridging data sharing and analyses while keeping data local, but heterogeneity across data sources and their impact on predictions is still a challenge.
ENSEMBLE LEARNING MODEL FOR SCREENING AUTISM IN CHILDRENijcsit
Autistic Spectrum Disorder (ASD) is a neurological condition associated with communication, repetitive,
and social challenges. ASD screening is the process of detecting potential autistic traits in individuals
using tests conducted by a medical professional, a caregiver, or a parent. These tests often contain large
numbers of items to be covered by the user and they generate a score based on scoring functions designed
by psychologists and behavioural scientists. Potential technologies that may improve the reliability and
accuracy of ASD tests are Artificial Intelligence and Machine Learning. This paper presents a new
framework for ASD screening based on Ensembles Learning called Ensemble Classification for Autism
Screening (ECAS). ECAS employs a powerful learning method that considers constructing multiple
classifiers from historical cases and controls and then utilizes these classifiers to predict autistic traits in
test instances. ECAS performance has been measured on a real dataset related to cases and controls of
children and using different Machine Learning techniques. The results revealed that ECAS was able to
generate better classifiers from the children dataset than the other Machine Learning methods considered
in regard to levels of sensitivity, specificity, and accuracy.
A Survey on Various Disease Prediction Techniquesijtsrd
An analysis of various diseases have been predicted using multiple data mining and text mining techniques. In this article we are going to discuss about 6 prediction techniques. Using gene expression pattern we predict the disease outcome and implementation of pathway based approach for classifying disease based on hyper box principles, we also present a novel hybrid prediction model with missing value imputation HPM-MI which analyze imputation using simple k-means clustering. A technique based on CCAR Constraint Class Association Rule has been used for reducing time consumption in prediction of a particular disease. We have discussed about text mining technique and their applications. Another technique has also been studied about hyper triglyceride mia from anthropometric measures which diverge according to age and gender. Using multilayer classifiers for disease prediction we can achieve high diagnosis accuracy and high performance. C. Leancy Jannet | G. V. Sumalatha "A Survey on Various Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18624.pdf
Genetic algorithms and feature selection techniques are used to analyze medical diagnosis data and predict disease. The process involves obtaining patient data, selecting relevant features, and using a genetic algorithm to evolve a mathematical model for accurate prediction. Specifically, (1) medical records are collected as training data, (2) irrelevant variables are removed via filter feature selection, and (3) a genetic algorithm simulates natural selection to iteratively improve a model for predicting disease in new patient records. This automated approach helps analyze large datasets, minimize human interaction, and facilitate timely treatment recommendations.
Machine Learning for Preclinical ResearchPaul Agapow
This document summarizes a presentation on machine learning for preclinical research. It discusses how biomedical data sets are often small and discusses challenges in applying deep learning and other machine learning techniques with limited data. It proposes combining multiple smaller datasets using standards to create larger datasets for analysis. The document also notes issues with noise and bias in biomedical data and proposes careful curation and appropriate analysis methods. In conclusion, it advocates for carefully curated combined datasets, integrating different data types and sources, and validated application of machine learning to support preclinical research.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Healthcare Predicitive Analytics for Risk Profiling in Chronic Care: A Bayesi...MIS Quarterly
This document describes a study that developed a Bayesian multitask learning (BMTL) model to predict multiple chronic disease risks using electronic health record data. Specifically, the model aimed to predict risks of stroke, heart attack, and kidney failure in patients with diabetes. The study evaluated the BMTL model against single-task learning baselines and other multitask learning approaches, finding the BMTL model achieved better predictive performance. A counterfactual analysis also suggested the BMTL model could help identify more patients for preventive treatment interventions compared to current practice.
Workshop on "Building Successful Pipelines for Predictive Analytics in Healthcare" delivered by Danielle Belgrave, PhD, Researcher at Microsoft Research, Cambridge, UK.
Clinical data analytics is an exciting new area of healthcare data analytics. This presentation presents a brief overview of the topic as an introduction and whetting the curiosity of the reader.
Big Data Provides Opportunities, Challenges and a Better Future in Health and...Cirdan
This presentation was delivered by Ashraf Mina, NSW Pathology at the Pathology Horizons 2017 Conference in Cairns, Australia.
Pathology Horizons 2017 is an annual CPD conference organised by Cirdan on the future of pathology. You can access more information about the event at www.pathologyhorizons.com
The company was founded in 2010 and is headquartered in Lisburn, Northern Ireland and has additional offices in Canada and Australia.
Cirdan is also responsible for organising Pathology Horizons, an annual and open CPD conference on the future of pathology. For more information visit - www.pathologyhorizons.com
Dichotomania and other challenges for the collaborating biostatisticianLaure Wynants
Conference presentation at ISCB 41 in the session
"Biostatistical inference in practice: moving beyond false
dichotomies"
A comment in Nature, signed by over 800 researchers, called for the scientific community to “retire statistical significance”. The responses included a call to halt the use of the term „statistically significant”, and changes in journal’s author guidelines. The leading discourse among statisticians is that inadequate statistical training of clinical researchers and publishing practices are to blame for the misuse of statistical testing. In this presentation, we search our collective conscience by reviewing ethical guidelines for statisticians in light of the p-value crisis, examine what this implies for us when conducting analyses in collaborative work and teaching, and whether the ATOM (accept uncertainty; be thoughtful, open and modest) principles can guide us.
Str-AI-ght to heaven? Pitfalls for clinical decision support based on AIBenVanCalster
This document summarizes some of the key pitfalls and challenges of using artificial intelligence (AI), particularly machine learning and deep learning models, for clinical decision support. It notes that (1) methodology is often poor, with small datasets and a lack of validation; (2) there is little evidence that most models actually improve outcomes; and (3) models show significant heterogeneity and may not generalize across settings and populations. It also discusses issues of proprietary datasets and models, conflicts of interest, and the challenges of actual implementation and assessing real-world impact. The document emphasizes that while AI has potential, more rigorous research is needed to develop trustworthy models that provide reliable decision support for patients and clinicians.
How to establish and evaluate clinical prediction models - StatsworkStats Statswork
A clinical prediction model can be used in various clinical contexts, including screening for asymptomatic illness, forecasting future events such as disease, and assisting doctors in their decision-making and health education. Despite the positive effects of clinical prediction models on practice, prediction modelling is a difficult process that necessitates meticulous statistical analysis and sound clinical judgments. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following always on Time, outstanding customer support, and High-quality Subject Matter Experts.
Read More With Us: https://bit.ly/3dxn32c
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics across Methodologies | Wide Range of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
United Kingdom: 44-1143520021
India: 91-4448137070
WhatsApp: 91-8754446690
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...CSCJournals
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
This document presents a comparative study of various data mining techniques for predicting heart disease using collected and standard heart disease datasets. Five classifiers - KStar, J48, SMO, Bayes Net, and MLP - were evaluated based on their accuracy and training time. SMO had the highest accuracy of 84-89% and MLP had the lowest training time of 0.33-0.75 seconds. The techniques are also compared based on their average classification variance. The study concludes with receiver operating characteristic curves showing the performance of the techniques on the two datasets.
Development and evaluation of prediction models: pitfalls and solutions (Part...BenVanCalster
Slides for the statistics in practice session for the Biometrisches Kolloquium (organized by the Deutsche Region der Internationalen Biometrischen Gesellschaft), 16 March 2021.
Part I from Maarten van Smeden: https://www.slideshare.net/MaartenvanSmeden/development-and-evaluation-of-prediction-models-pitfalls-and-solutions
Disease Prediction And Doctor Appointment systemKOYELMAJUMDAR1
This document outlines a disease prediction and doctor appointment system using machine learning. The objectives are to provide quick medical diagnosis to rural patients and enhance access to medical specialists. Five machine learning algorithms - Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine - are used for disease prediction. The system displays predicted diseases and accuracy scores for each algorithm. Users can then book appointments with specialist doctors for their predicted disease.
A Survey on Heart Disease Prediction Techniquesijtsrd
Heart disease is the main reason for a huge number of deaths in the world over the last few decades and has evolved as the most life threatening disease. The health care industry is found to be rich in information. So, there is a need to discover hidden patterns and trends in them. For this purpose, data mining techniques can be applied to extract the knowledge from the large sets of data. Many researchers, in recent times have been using several machine learning techniques for predicting the heart related diseases as it can predict the disease effectively. Even though a machine learning technique proves to be effective in assisting the decision makers, still there is a scope for developing an accurate and efficient system to diagnose and predict the heart diseases thereby helping doctors with ease of work. This paper presents a survey of various techniques used for predicting heart disease and reviews their performance. G. Niranjana | Dr I. Elizabeth Shanthi "A Survey on Heart Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38349.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38349/a-survey-on-heart-disease-prediction-techniques/g-niranjana
Open science LMU session contribution E Steyerberg 2jul20Ewout Steyerberg
1. The document discusses open science approaches to addressing big research questions through multiple analysts studying the same dataset and research question, as well as data and analysis sharing initiatives.
2. It describes challenges in open science including variation in analyses and interpretations due to heterogeneity across datasets and studies.
3. Initiatives like OHDSI are highlighted as bridging data sharing and analyses while keeping data local, but heterogeneity across data sources and their impact on predictions is still a challenge.
ENSEMBLE LEARNING MODEL FOR SCREENING AUTISM IN CHILDRENijcsit
Autistic Spectrum Disorder (ASD) is a neurological condition associated with communication, repetitive,
and social challenges. ASD screening is the process of detecting potential autistic traits in individuals
using tests conducted by a medical professional, a caregiver, or a parent. These tests often contain large
numbers of items to be covered by the user and they generate a score based on scoring functions designed
by psychologists and behavioural scientists. Potential technologies that may improve the reliability and
accuracy of ASD tests are Artificial Intelligence and Machine Learning. This paper presents a new
framework for ASD screening based on Ensembles Learning called Ensemble Classification for Autism
Screening (ECAS). ECAS employs a powerful learning method that considers constructing multiple
classifiers from historical cases and controls and then utilizes these classifiers to predict autistic traits in
test instances. ECAS performance has been measured on a real dataset related to cases and controls of
children and using different Machine Learning techniques. The results revealed that ECAS was able to
generate better classifiers from the children dataset than the other Machine Learning methods considered
in regard to levels of sensitivity, specificity, and accuracy.
A Survey on Various Disease Prediction Techniquesijtsrd
An analysis of various diseases have been predicted using multiple data mining and text mining techniques. In this article we are going to discuss about 6 prediction techniques. Using gene expression pattern we predict the disease outcome and implementation of pathway based approach for classifying disease based on hyper box principles, we also present a novel hybrid prediction model with missing value imputation HPM-MI which analyze imputation using simple k-means clustering. A technique based on CCAR Constraint Class Association Rule has been used for reducing time consumption in prediction of a particular disease. We have discussed about text mining technique and their applications. Another technique has also been studied about hyper triglyceride mia from anthropometric measures which diverge according to age and gender. Using multilayer classifiers for disease prediction we can achieve high diagnosis accuracy and high performance. C. Leancy Jannet | G. V. Sumalatha "A Survey on Various Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18624.pdf
Genetic algorithms and feature selection techniques are used to analyze medical diagnosis data and predict disease. The process involves obtaining patient data, selecting relevant features, and using a genetic algorithm to evolve a mathematical model for accurate prediction. Specifically, (1) medical records are collected as training data, (2) irrelevant variables are removed via filter feature selection, and (3) a genetic algorithm simulates natural selection to iteratively improve a model for predicting disease in new patient records. This automated approach helps analyze large datasets, minimize human interaction, and facilitate timely treatment recommendations.
Machine Learning for Preclinical ResearchPaul Agapow
This document summarizes a presentation on machine learning for preclinical research. It discusses how biomedical data sets are often small and discusses challenges in applying deep learning and other machine learning techniques with limited data. It proposes combining multiple smaller datasets using standards to create larger datasets for analysis. The document also notes issues with noise and bias in biomedical data and proposes careful curation and appropriate analysis methods. In conclusion, it advocates for carefully curated combined datasets, integrating different data types and sources, and validated application of machine learning to support preclinical research.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document presents a methodology for developing a deep learning framework for automated multi-class diagnosis of neurological disorders using MRI images. The researchers consolidated MRI datasets for diseases like brain tumors, Parkinson's, and MS. They trained a ResNet50 model on over 2000 MRI images and achieved 83.69% test accuracy in distinguishing tumors, Parkinson's and normal cases. They propose expanding the framework to diagnose more neurological disorders and developing a computer-aided diagnosis tool to assist neurologists.
This document summarizes recent research on applications of artificial intelligence (AI) in healthcare. It discusses how AI is being used to analyze different types of healthcare data, such as images, genetic data, clinical notes, and more, using techniques like machine learning and natural language processing. Popular applications of AI have involved diseases like cancer, neurology disorders, and cardiovascular issues, by helping with tasks like early detection, diagnosis, and treatment optimization. While AI shows promise in assisting doctors, the document concludes that AI will not replace human physicians but may help with some functional areas by providing insights from large datasets.
Artificial intelligence in healthcare past,present and futureErrepe
This document discusses artificial intelligence (AI) applications in healthcare. It surveys the current status of AI in healthcare and major disease areas where AI is used, including cancer, neurology, and cardiology. The document also reviews AI applications in stroke, including early detection and diagnosis, treatment, and outcome prediction. Popular AI techniques discussed are machine learning methods for structured data like images and neural networks, and natural language processing for unstructured data like clinical notes. The document concludes that while AI cannot replace physicians, it can assist clinicians by analyzing large amounts of healthcare data to support clinical decision making.
Artificial intelligence in medicine (projeck)YasserAli152984
The document discusses various uses of artificial intelligence in medicine, including disease detection, diagnostics, scientific experiments, surgery robots, and cancer detection. It notes that AI has made progress in areas like analyzing large datasets, aiding physicians, and automating administrative tasks. However, the integration of human and AI is seen as key to revolutionizing healthcare.
This document discusses the application of machine learning in healthcare. It begins with an introduction of the author and their background in machine learning engineering. It then discusses the UN Sustainable Development Goals around health and highlights non-communicable and infectious diseases as areas machine learning could help address. The document outlines how machine learning can help expand medical knowledge, disseminate information, enable personalized medicine, and increase patient engagement. It also discusses best practices for business understanding, data modeling, and feature engineering when applying machine learning in healthcare.
This document summarizes research on using machine learning techniques and IoMT to develop a heart disease diagnostic system. It discusses previous work that used algorithms like logistic regression, decision trees, random forest, SVM, etc. to predict heart disease with accuracies ranging from 70% to 95.25%. The document then proposes a novel technique that independently implements majority voting with 5 machine learning algorithms to obtain a promising accuracy of 88.59% for heart disease prediction, outperforming existing approaches. It also describes the dataset and classification techniques used in the proposed system.
K-Nearest Neighbours based diagnosis of hyperglycemiaijtsrd
This document summarizes a research paper that developed an artificial intelligence system using the K-nearest neighbors algorithm to diagnose hyperglycemia (high blood sugar). The system was trained on a database of 415 patient cases characterized by 10 physiological parameters. It achieved a diagnostic accuracy of 91% compared to medical experts when tested on new patient data. The authors conclude the KNN-based system is useful for diabetes diagnosis and could help supplement medical doctors, especially in remote areas with limited access to experts.
This document presents an overview of the AI applications in life sciences. The presentation highlights various steps in drug development and AI applications. Also, discusses Alzheimer’s disease and obstacles to develop drugs. Finally, presents details of AI in target identification for AD.
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1. Use of Artificial Intelligence in
AAOCA
Jai Nahar, MD, MBA
Associate Professor of Pediatrics
The GeorgeWashington University School of
Medicine and Health Sciences
Division of Cardiology
Children’s National Medical Center
Washington, DC
Congenital Heart Surgeons Society
Fall work weekend
Nov. 2017,Toronto
3. Agenda
1. AAOCA: Background, problem, questions to be addressed
2. Current Opportunity
3. Introduction to AI in medicine
4. PotentialApplication ofAI in AAOCA
5. Challenges
6. Future directions
4. AAOCA: Background
• Second leading cause of SCD in young athletes in USA
• Care of these patients involve three important challenges
1. Diagnosis: incidental diagnosis, lack of symptoms, initial presentation may
be SCD or sudden cardiac arrest
2. Risk Stratification: phenotypic heterogeneity
3. Ideal management
Risk
Stratification
Diagnosis
AAOCA
Ideal
Management
5. AAOCA:What causes the Critical Lethal event?
Critical Event :
Coronary ischemia
and lethal
Ventricular
Tachyarrhythmia
Decreased
coronary perfusion
due to Mechanical
factors, altered
blood flow
dynamics
Electrically unstable
myocardial
substrate
Unknown
Factors
?
• Acute angle take off and
Kinking of coronary artery at
its origin
• Flap like closure of abnormal
slit like coronary orifice
• Compression of anomalous
coronary artery between
Aorta and Pulmonary artery
during exercise
• Spasm of anomalous coronary
artery possibly secondary to
endothelial injury
Prior intermittent ischemia
Patchy Myocardial Fibrosis
• Local myocardial
metabolic factors
• Channelopathies
• Genetic
predisposition to
arrhythmia
Basso et al. (2000). Clinical profile of congenital coronary artery
anomalies with origin from the wrong aortic sinus leading to sudden
death in young competitive athletes. Journal of the American College of
Cardiology, 35(6), 1493-1501.
6. Problem
There are knowledge gaps/unknown variables which need to be filled/uncovered
to refine our risk stratification for AAOCA patients who are at highest risk of
sudden death
• Asymptomatic (hidden)
• Gray zone (symptoms of ? significance)
7. Task: Making the invisible, visible
AAOCA: Myocardial
ischemia, malignant
Ventricular
arrhythmia, sudden
death
Unknown/Invisible?
• Genetic make up
• Channelopathies
• Local myocardial/metabolic
factors
• Etc ??????
8. Questions
1. How can we uncover currently unknown phenotypes (cluster of variables with
non linear relation) which indicate high risk of sudden cardiac death?
2. How to develop good predictive models for risk stratification and prompt
detection of high risk AAOCA ?
9. Call for Action:Two step approach
Uncover unknown high risk
groups
Develop refined risk
stratification models
Deep
Phenotyping
10. Agenda
1. Define the problem, and questions to be addressed
2. Current Opportunity
3. Introduction to AI in medicine
4. Potential Application of AI to AAOCA
5. Challenges
6. Future directions
11. Current Opportunity : make the invisible, visible
• Data: CHSS AAOCA registry
• Artificial Intelligence Methods
Power of
Data
Leverage
AI
Refined risk
stratification
and patient
management
12. Dawn of New Era of
Augmented Intelligence
Physician and AI ( Human/Machine) synergy for facilitating better
• Diagnosis
• Disease management
• Clinical Decisions
Physicians Machines
Platform for
Precision Medicine
13. Agenda
1. Define the problem, and questions to be addressed
2. Current Opportunity
3. Introduction to AI in medicine
4. Potential Application of AI to AAOCA
5. Challenges
6. Future directions
14. Medical Intelligence: AI in Medicine
Big Data and Artificial Intelligence in Pediatric Cardiology
Anthony C. Chang, MD, MBA, MPH
16. Machine Learning
Andrew Ng
“Is the Science of getting computers to learn, without being explicitly programmed”
Full spectrum application in CV medicine
Machine
Learning
Diagnosis/Risk
stratification
Treatment
Prevention
Research
18. Supervised Learning
• Goal is to predict a known output orTarget
• Algorithm is taught with right answers (labels) for examples used in
training data set.
• Can be categorized as:
Classification problem: predicting categories, discrete value output
(0,1 etc.)
Regression problem: predict continuous values
Anomaly detection problem: predict unusual pattern
21. Unsupervised Learning
• Goal is to learn the intrinsic structure within data.
• No outputs to predict
• Task is to find hidden pattern/structure in data,
without human feedback
Cluster2
Cluster3
Cluster
1
22. Unsupervised Learning Application
Identify novel disease mechanisms, genotypes,
phenotypes: Filling the knowledge gaps
Identify Novel
Disease
Mechanisms
New
paths/approach
to therapy
Precision
Medicine
initiative
23. Unsupervised Learning Algorithms
• Clustering algorithms: K-Means, Hierarchical clustering
Used to cluster unlabeled data into different groups
Used when no obvious natural grouping
• Association rule- learning algorithm:
Help to uncover relationships between seemingly unrelated data items
• PrincipalComponent Analysis
• Sparse Coding
25. K-Means clustering Algorithms
(left) K-means in 2d. (right) K-means in 3d. You have
to imagine k-means in 4d.
http://stanford.edu/~cpiech/cs221/handouts/kmeans.html
26. Reinforcement learning
Reinforcement learning led to AlphaGo’s stunning victory over a human Go
champion
https://www.technologyreview.com/s/603501/10-breakthrough-technologies-2017-reinforcement-
learning/
Reinforcement learning is
learning by trial-and-error,
solely from rewards or
punishments.
https://deepmind.com/blog/deep-reinforcement-
learning/
Can be viewed as hybrid
of supervised and
unsupervised learning
27. Artificial Neural Network (ANN)
ANN are modeled after human neurons
• Nodes are like neurons
• Input layer: input data/ predictor variables/ features
• Hidden layer: processing of input
• Output layer:Target (prediction of class or value)
Lee, J.-G., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep Learning in Medical Imaging: General
Overview. Korean Journal of Radiology, 18(4), 570–584. http://doi.org/10.3348/kjr.2017.18.4.570
28. Deep Learning
• Part of Machine Learning
• Uses multiple layers of ANN
• Mimics the working of human brain
Lee, J.-G., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep Learning in Medical Imaging: General
Overview. Korean Journal of Radiology, 18(4), 570–584. http://doi.org/10.3348/kjr.2017.18.4.570
29. Deep Learning
Three types of Network:
1. Deep Neural Networks: Google’s Deep mind Alpha Go
2. Recurrent Neural Networks (RNN): natural language
processing, handwriting and speech recognition
3. Convolutional Neural Networks (CNN): Computer
vision, image recognition, CV imaging
30. Cognitive Computing
Systems mimicking human cognition, help
replicate human capabilities across the spectrum of sensory
perception, deduction, reasoning, learning and knowledge.
31. Upcoming inWorld of AI
• Quantum Computing: accelerate the data processing speed
• Neuromorphic chips
• Partnership with other technologies
AI and AR
AI and Robotics
32. Agenda
1. AAOCA: Background, problem, questions to be addressed
2. Current Opportunity
3. Introduction to AI in medicine
4. Potential Application of AI in AAOCA
5. Challenges
6. Future directions
33. First : A Machine Learning overview
Machine Learning in Medicine
Rahul C. Deo
Circulation. 2015;132:1920-1930, originally published November 16, 2015
https://doi.org/10.1161/CIRCULATIONAHA.115.001593
A: Matrix representation of the supervised
and unsupervised learning problem.
B: Decision trees map features to
outcome (Supervised Learning)
C: Neural networks predict outcome based
on transformed representations of
features. (Supervised Learning)
D:The k-nearest neighbor algorithm
assigns class based on the values of the
most similar training examples.
(Supervised Learning)
34. Phenomapping
Use of unsupervised machine learning in Phenotypic
classification of a heterogeneous clinical syndrome into discrete
phenogroups
PG1
PG2
PG3
PG4
PG5
Phenomapping
35. Phenotypic classification (Phenomapping)
A: Phenotype heat map of HFpEF.
B: BIC analysis for the identification of the
optimal number of phenotypic clusters
(pheno groups).
C: Kaplan–Meier curves
Survival free of cardiovascular (CV)
hospitalization or death stratified by
pheno groups.
Machine Learning in Medicine
Rahul C. Deo
Circulation. 2015;132:1920-1930
36. Stepwise approach for AAOCA
Detection of
High Risk
Cases
Develop
Supervised
model for
disease
prediction
Novel
phenogroup
selection using
Unsupervised
learning
37. Proposed Framework for AAOCA
Demographic
Data
Stress
lab/Nuclear
Medicine
Data
EP data
Surgical
data
Cath data
Imaging
data
Clinical
Data
Unsupervised learning based
Pheno-groups identification
Supervised Learning
based Model for
Risk stratification
High
Medium
Low
Expert
Knowledge
38. Challenges
• Deep learning:
Problem of model overfitting
Need for large data sets, multi Institutional collaboration
Setting a neural network is time consuming
• Need for sharing of machine learning expertise
• Need for Novel informative features to build improved models
• Need for refined biomarkers to access myocardial status
39. Future Directions
Seamless data collaboration across institutions, and enhanced Computer- Human
synergy lead Augmented intelligence will refine the path of precisionCardiovascular
Medicine.
Precision
Medicine
Big Data
Collaboration
Augmented
Intelligence
(Human/Machine
Partnership)
40. Conclusion
To optimally harness the power of Big Data and AI in
Medicine we need Multi Institutional collaboration
between Physicians, Data Scientists and Machine
Learning Experts.
42. References
1. Basso, C., Maron, B. J., Corrado, D., & Thiene, G. (2000). Clinical profile of congenital coronary artery anomalies with origin from the wrong aortic
sinus leading to sudden death in young competitive athletes. Journal of the American College of Cardiology, 35(6), 1493-1501.
2. Betancur, J., Otaki, Y., Motwani, M., Fish, M. B., Lemley, M., Dey, D., . . . Sharir, T. (2017). Prognostic value of combined clinical and myocardial
perfusion imaging data using machine learning. JACC: Cardiovascular Imaging,
3. Brothers, J. A. (2017). Introduction to anomalous aortic origin of a coronary artery. Congenital Heart Disease, 12(5), 600-602. doi:10.1111/chd.12497
4. Chang, A. C. (2016). Big data in medicine: The upcoming artificial intelligence. Progress in Pediatric Cardiology, 43, 91-94.
5. Chang, A. P., & Musen, M. (2012). Artificial Intelligence in Pediatric Cardiology: An Innovative Transformation in Patient Care, Clinical Research, and
Medical Education. Congenital Cardiology, 10, 1-9
6. Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920-1930. doi:10.1161/CIRCULATIONAHA.115.001593 [doi]
7. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the
American College of Cardiology, 69(21), 2657-2664.
8. Lee, J., Jun, S., Cho, Y., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep learning in medical imaging: General overview. Korean Journal of
Radiology, 18(4), 570-584.
9. Maron, B. J., Doerer, J. J., Haas, T. S., Tierney, D. M., & Mueller, F. O. (2009). Sudden deaths in young competitive athletes: Analysis of 1866 deaths
in the united states, 1980-2006. Circulation, 119(8), 1085-1092. doi:10.1161/CIRCULATIONAHA.108.804617 [doi]
10. Shah, S. J., Katz, D. H., Selvaraj, S., Burke, M. A., Yancy, C. W., Gheorghiade, M., . . . Deo, R. C. (2015). Phenomapping for novel classification of
heart failure with preserved ejection fraction. Circulation, 131(3), 269-279. doi:10.1161/CIRCULATIONAHA.114.010637 [doi]
11. Van Hare, G. F., Ackerman, M. J., Evangelista, J. A., Kovacs, R. J., Myerburg, R. J., Shafer, K. M., . . . American Heart Association Electrocardiography
and Arrhythmias Committee of Council on Clinical Cardiology, Council on Cardiovascular Disease in Young, Council on Cardiovascular and Stroke
Nursing, Council on Functional Genomics and Translational Biology, and American College of Cardiology. (2015). Eligibility and disqualification
recommendations for competitive athletes with cardiovascular abnormalities: Task force 4: Congenital heart disease: A scientific statement from
the american heart association and american college of cardiology. Circulation, 132(22), e281-91. doi:10.1161/CIR.0000000000000240 [doi]
Two step approach
Using unsupervised learning, uncover unknown high risk patterns, groups within the data
Evaluate their performance in subsequent supervised learning tasks (how useful these new patterns are to AAOCA). This can help in developing refined risk stratification models, facilitating precision medicine
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664.
This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.
Supervised learning is so named because the data scientist acts as a guide to teach the algorithm what conclusions it should come up with. It’s similar to the way a child might learn arithmetic from a teacher. Supervised learning requires that the algorithm’s possible outputs are already known and that the data used to train the algorithm is already labeled with correct answers. For example, a classification algorithm will learn to identify animals after being trained on a dataset of images that are properly labeled with the species of the animal and some identifying characteristics.
Su[prevised learning algorithms have been applied to problems in prediction, diagnosis and treatment of CVD
Prediction accuracy depends on:
Algorithm used
Dataset
hypothesis
K means clustering: Data is clustered in K classes by K-Means Algorithm
Hierarchical clustering: can be two types
Agglomerative approach: Bottom up approach
Divisive: Top down approach
https://www.youtube.com/watch?v=RD0nNK51Fp8 (Stanford site)
An ANN is comprised of a network of artificial neurons (also known as "nodes"). These nodes are connected to each other, and the strength of their connections to one another is assigned a value based on their strength: inhibition (maximum being -1.0) or excitation (maximum being +1.0). If the value of the connection is high, then it indicates that there is a strong connection. Within each node's design, a transfer function is built in. There are three types of neurons in an ANN, input nodes, hidden nodes, and output nodes
Three types of deep learning
http://researcher.watson.ibm.com/researcher/view_group.php?id=4384
Medical Sieve is an ambitious long-term exploratory grand challenge project to build a next generation cognitive assistant with advanced multimodal analytics, clinical knowledge and reasoning capabilities that is qualified to assist in clinical decision making in radiology and cardiology. It will exhibit a deep understanding of diseases and their interpretation in multiple modalities (X-ray, Ultrasound, CT, MRI, PET, Clinical text) covering various radiology and cardiology specialties. The project aims at producing a sieve that filters essential clinical and diagnostic imaging information to form anomaly-driven summaries and recommendations that tremendously reduce the viewing load of clinicians without negatively impacting diagnosis.
http://circ.ahajournals.org/content/132/20/1920.short
Machine learning overview. A, Matrix representation of the supervised and unsupervised learning problem. We are interested in developing a model for predicting myocardial infarction (MI). For training data, we have patients, each characterized by an outcome (positive or negative training examples), denoted by the circle in the right-hand column, and by values of predictive features, as well, denoted by blue to red coloring of squares. We seek to build a model to predict outcome by using some combination of features. Multiple types of functions can be used for mapping features to outcome (B through D). Machine learning algorithms are used to find optimal values of free parameters in the model to minimize training error as judged by the difference between predicted values from our model and actual values. In the unsupervised learning problem, we are ignoring the outcome column and grouping together patients based on similarities in the values of their features. B, Decision trees map features to outcome. At each node or branch point, training examples are partitioned based on the value of a particular feature. Additional branches are introduced with the goal of completely separating positive and negative training examples. C, Neural networks predict outcome based on transformed representations of features. A hidden layer of nodes integrates the value of multiple input nodes (raw features) to derive transformed features. The output node then uses values of these transformed features in a model to predict outcome. D, The k-nearest neighbor algorithm assigns class based on the values of the most similar training examples. The distance between patients is computed based on comparing multidimensional vectors of feature values. In this case, where there are only 2 features, if we consider the outcome class of the 3 nearest neighbors, the unknown data instance would be assigned a “no MI” class. LDL indicates low-density lipoprotein; and MI, myocardial infarction.
Application of unsupervised learning to HFpEF. A, Phenotype heat map of HFpEF. Columns represent individual study participants; rows represent individual features. B, Bayesian information criterion analysis for the identification of the optimal number of phenotypic clusters (pheno groups). C, Survival free of cardiovascular (CV) hospitalization or death stratified by phenotypic cluster. Kaplan–Meier curves for the combined outcome of heart failure hospitalization, cardiovascular hospitalization, or death stratified by phenotypic cluster
AAOCA Data: Multidimensional with Phenotypic Heterogeneity