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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 FREE CHAPTER 2. Healthcare Foundations 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

Introduction to predictive analytics in healthcare

In Chapter 1, Introduction to Healthcare Analytics, we discussed the three subcomponents of analytics: descriptive analytics, predictive analytics, and prescriptive analytics. Predictive and prescriptive analytics form the heart of healthcare's mission to improve care, cost, and outcomes. That is because if we can predict that an adverse event is likely in the future, we can divert our scarce resources toward preventing the adverse event from occurring.

What are some of the adverse events we can predict (and then prevent) in healthcare?

  • Deaths: Obviously, any death that is preventable or foreseeable should be avoided. Once a death is predicted to occur, preventative actions may include directing more nurses toward that patient, hiring more consultants for the case, or speaking to the family about options earlier rather than...
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