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Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series) - Tapa dura
Sinopsis
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.
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Thomas A. Gerds is professor at the biostatistics unit at the University of Copenhagen. He is affiliated with the Danish Heart Foundation. He is author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years.
Michael 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.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series)
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Chapman and Hall/CRC 2020-12-18, 2020
ISBN 10: 113838447X
ISBN 13: 9781138384477
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Hardcover. Condición: New. Nº de ref. del artículo: 6666-TNFPD-9781138384477
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Medical Risk Prediction Models
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Thomas A. Gerds is professor at the biostatistics unit at the University of Copenhagen. He is affiliated with the Danish Heart Foundation. He is author of several R-packages on CRAN and has taught statistics courses to non-statisticians . Nº de ref. del artículo: 395504665
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Medical Risk Prediction Models
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Chapman And Hall/CRC Feb 2021, 2021
ISBN 10: 113838447X
ISBN 13: 9781138384477
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 314 pp. Englisch. Nº de ref. del artículo: 9781138384477
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Medical Risk Prediction Models : With Ties to Machine Learning
Publicado por
Chapman and Hall/CRC, 2021
ISBN 10: 113838447X
ISBN 13: 9781138384477
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Medical Risk Prediction Models : With Ties to Machine Learning
Publicado por
Chapman and Hall/CRC, 2021
ISBN 10: 113838447X
ISBN 13: 9781138384477
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Medical Risk Prediction Models : With Ties to Machine Learning
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Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series)
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Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series)
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Medical Risk Prediction Models : With Ties to Machine Learning
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Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9781138384477
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Medical Risk Prediction Models: With Ties to Machine Learning (Chapman & Hall/CRC Biostatistics Series)
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