In this chapter, we have toured some of the machine learning and mathematical foundations for performing healthcare analytics. In the next chapter, we'll continue exploring the foundational triumvirate of healthcare analytics by moving on to the computing leg.
Healthcare Analytics Made Simple
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Healthcare Analytics Made Simple
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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)
Preface
Free Chapter
Introduction to Healthcare Analytics
Healthcare Foundations
Machine Learning Foundations
Computing Foundations – Databases
Computing Foundations – Introduction to Python
Measuring Healthcare Quality
Making Predictive Models in Healthcare
Healthcare Predictive Models – A Review
The Future – Healthcare and Emerging Technologies
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