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Healthcare Analytics Made Simple

You're reading from   Healthcare Analytics Made Simple Techniques in healthcare computing using machine learning and Python

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781787286702
Length 268 pages
Edition 1st Edition
Languages
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Authors (2):
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Vikas (Vik) Kumar Vikas (Vik) Kumar
Author Profile Icon Vikas (Vik) Kumar
Vikas (Vik) Kumar
Shameer Khader Shameer Khader
Author Profile Icon Shameer Khader
Shameer Khader
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Table of Contents (11) Chapters Close

Preface 1. Introduction to Healthcare Analytics 2. Healthcare Foundations FREE CHAPTER 3. Machine Learning Foundations 4. Computing Foundations – Databases 5. Computing Foundations – Introduction to Python 6. Measuring Healthcare Quality 7. Making Predictive Models in Healthcare 8. Healthcare Predictive Models – A Review 9. The Future – Healthcare and Emerging Technologies 10. Other Books You May Enjoy

Predictive healthcare analytics – state of the art

As we touched upon in Chapter 3, Machine Learning Foundations, healthcare is no stranger to complex risk factor assessments. For almost every major disease, one can find several risk-scoring models that are used widely by physicians to assess the risk of having a disease or suffering morbidity/mortality from that disease. When we use the term "risk score," we are largely referring to criterion tables, in which risk factors are allotted point values, and the points for all of the risk factors are summed to give an overall risk based on the total. These scoring systems are used widely in medicine; interestingly, many of them are based on research involving logistic regression models (similar to the one developed in Chapter 7, Making Predictive Models in Healthcare). The crucial question of the last several decades...

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