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Medical Risk Prediction Models

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Medical Risk Prediction Models

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Author : Thomas A. Gerds
language : en
Publisher: CRC Press
Release Date : 2021-02-01

Medical Risk Prediction Models written by Thomas A. Gerds and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-01 with Mathematics categories.

Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination, calibration, and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service, and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research.

Medical Risk Prediction Models

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Author : Thomas A. Gerds
language : en
Publisher: CRC Press
Release Date : 2021-02-01

Healthcare Risk Adjustment And Predictive Modeling

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Author : Ian G. Duncan
language : en
Publisher: ACTEX Publications
Release Date : 2011

Healthcare Risk Adjustment And Predictive Modeling written by Ian G. Duncan and has been published by ACTEX Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Business & Economics categories.

This text is listed on the Course of Reading for SOA Fellowship study in the Group & Health specialty track. Healthcare Risk Adjustment and Predictive Modeling provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare, disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides access to a test dataset.

Clinical Prediction Models

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Author : Ewout W. Steyerberg
language : en
Publisher: Springer
Release Date : 2019-07-22

Clinical Prediction Models written by Ewout W. Steyerberg and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-22 with Medical categories.

The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies

Derivation And Validation Of A Time Dependent Risk Prediction Model For In Hospital Mortality

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Author : Jenna Chun-Lay Wong
language : en
Publisher:
Release Date : 2010

Derivation And Validation Of A Time Dependent Risk Prediction Model For In Hospital Mortality written by Jenna Chun-Lay Wong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Health risk assessment categories.

Accurate risk prediction models for in-hospital mortality are important for unbiased comparisons of hospital performance (by producing risk-adjusted mortality rates) and improved patient outcomes (by identifying high-risk patients in need of special medical attention). No previous risk prediction models have properly used post-admission information to predict risk of death in-hospital. In this study, we used administrative and laboratory data to derive and internally validate a Cox regression model (the "'Escobar' +" model) that predicts the risk of in-hospital death at any point during the admission. The model had excellent discrimination ('c'-statistic 0.895,95% confidence interval [CI] 0.889-0.902) and calibration. The 'Escobar'+ model is a powerful risk-adjustment methodology that can be used in studies where the start of observation occurs post-admission. The model could also improve the quality and timeliness of patient care by providing health care workers with highly specific and accurateestimates of in-hospital death risk during the patient's stay.

Fundamentals Of Clinical Data Science

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Author : Pieter Kubben
language : en
Publisher: Springer
Release Date : 2018-12-21

Fundamentals Of Clinical Data Science written by Pieter Kubben and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-21 with Medical categories.

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Leveraging Data Science For Global Health

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Author : Leo Anthony Celi
language : en
Publisher: Springer Nature
Release Date : 2020-07-31

Leveraging Data Science For Global Health written by Leo Anthony Celi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-31 with Medical categories.

This open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. It focuses on digital disease surveillance through the application of machine learning to non-traditional data sources. Developing countries are uniquely prone to large-scale emerging infectious disease outbreaks due to disruption of ecosystems, civil unrest, and poor healthcare infrastructure – and without comprehensive surveillance, delays in outbreak identification, resource deployment, and case management can be catastrophic. In combination with context-informed analytics, students will learn how non-traditional digital disease data sources – including news media, social media, Google Trends, and Google Street View – can fill critical knowledge gaps and help inform on-the-ground decision-making when formal surveillance systems are insufficient.

Prognosis Research In Healthcare

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Author : Richard D. Riley
language : en
Publisher: Oxford University Press
Release Date : 2019-01-17

Prognosis Research In Healthcare written by Richard D. Riley and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-17 with Medical categories.

"What is going to happen to me?" Most patients ask this question during a clinical encounter with a health professional. As well as learning what problem they have (diagnosis) and what needs to be done about it (treatment), patients want to know about their future health and wellbeing (prognosis). Prognosis research can provide answers to this question and satisfy the need for individuals to understand the possible outcomes of their condition, with and without treatment. Central to modern medical practise, the topic of prognosis is the basis of decision making in healthcare and policy development. It translates basic and clinical science into practical care for patients and populations. Prognosis Research in Healthcare: Concepts, Methods and Impact provides a comprehensive overview of the field of prognosis and prognosis research and gives a global perspective on how prognosis research and prognostic information can improve the outcomes of healthcare. It details how to design, carry out, analyse and report prognosis studies, and how prognostic information can be the basis for tailored, personalised healthcare. In particular, the book discusses how information about the characteristics of people, their health, and environment can be used to predict an individual's future health. Prognosis Research in Healthcare: Concepts, Methods and Impact, addresses all types of prognosis research and provides a practical step-by-step guide to undertaking and interpreting prognosis research studies, ideal for medical students, health researchers, healthcare professionals and methodologists, as well as for guideline and policy makers in healthcare wishing to learn more about the field of prognosis.

Long Term Care Providers And Services Users In The United States 2015 2016

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Author :
language : en
Publisher:
Release Date : 2019

Long Term Care Providers And Services Users In The United States 2015 2016 written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Handbook Of Research On Disease Prediction Through Data Analytics And Machine Learning

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Author : Rani, Geeta
language : en
Publisher: IGI Global
Release Date : 2020-10-16

Handbook Of Research On Disease Prediction Through Data Analytics And Machine Learning written by Rani, Geeta and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Medical categories.

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Medical Risk Prediction Models

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