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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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Healthcare delivery in the US

The healthcare industry impacts all of us, through its interactions with ourselves, our loved ones, our family, and our friends. The high costs associated with the healthcare industry are intertwined with the physical, emotional, and spiritual trauma that occurs when someone close to us becomes ill or feels pain.

In the United States, the healthcare system is in a fragile state, as healthcare expenditure exceeds 15% of the nation's total GDP; this proportion far exceeds that of other developed countries, and is expected to rise to at least 20% by the year 2040 (Braunstein, 2014; Bernaert, 2015). The rise in healthcare costs in the US, and internationally, can be attributed to several factors. One is a shift in demographics to a more elderly population. Average life expectancy (LE) rose to in excess of 80 years of age for the first time in 2011...

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