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Healthcare Analytics Made Simple

Healthcare Analytics Made Simple

By : Kumar, Khader
4.4 (8)
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Healthcare Analytics Made Simple

Healthcare Analytics Made Simple

4.4 (8)
By: Kumar, Khader

Overview of this book

In recent years, machine learning technologies and analytics have been widely utilized across the healthcare sector. Healthcare Analytics Made Simple bridges the gap between practising doctors and data scientists. It equips the data scientists’ work with healthcare data and allows them to gain better insight from this data in order to improve healthcare outcomes. This book is a complete overview of machine learning for healthcare analytics, briefly describing the current healthcare landscape, machine learning algorithms, and Python and SQL programming languages. The step-by-step instructions teach you how to obtain real healthcare data and perform descriptive, predictive, and prescriptive analytics using popular Python packages such as pandas and scikit-learn. The latest research results in disease detection and healthcare image analysis are reviewed. By the end of this book, you will understand how to use Python for healthcare data analysis, how to import, collect, clean, and refine data from electronic health record (EHR) surveys, and how to make predictive models with this data through real-world algorithms and code examples.
Table of Contents (11 chapters)
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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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