Medical Risk Prediction Models: With Ties to Machine Learning

Author:   Thomas A. Gerds ,  Michael W. Kattan
Publisher:   Taylor & Francis Ltd
ISBN:  

9781138384477


Pages:   312
Publication Date:   01 February 2021
Format:   Hardback
Availability:   In Print   Availability explained
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Medical Risk Prediction Models: With Ties to Machine Learning


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Author:   Thomas A. Gerds ,  Michael W. Kattan
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   0.589kg
ISBN:  

9781138384477


ISBN 10:   113838447
Pages:   312
Publication Date:   01 February 2021
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Software. 2. I am going to make a prediction model. What do I need to know? 3. Regression model. 4. How should I prepare for modeling? 5. I am ready to build a prediction model. 7. Does my model predict accurately? 7. How do I decide between rival models? 8. Can't the computer just take care of all of this? 9. Things you might have expected in our book.

Reviews

Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book. ~Donna Ankerst, Technical University of Munich Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book. ~Donna Ankerst, Technical University of Munich Overall, the book offers a well-written, complete and illustrative overview of clinical prediction models with clear stances and directions on the modelling methods, choices and strategies. I find this a very welcome and much needed addition to the literature because prediction is the backbone of medical decision-making; few books are dedicated to modelling strategies and artificial intelligence is ascending in medical research. I thereby highly recommend this book for anyone who would be interested in performing predictive modelling for prognostic or diagnostic research. -Evangelos I. Kritsotakis, International Society for Clinical Biostatistics, 72, 2021


Two of the top researchers in the field of clinical prediction models have produced a highly innovative book that brings a very technical topic to public grasp by throwing out the formulas and just talking straight from the heart of practical experience. While clinicians and medical residents can now learn how to build, diagnose and validate risk models themselves, all public health researchers, old and new, will reap the benefits and enjoyment from reading this book. ~Donna Ankerst, Technical University of Munich


Author Information

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.

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