Book Image

Healthcare Analytics Made Simple

By : Vikas (Vik) Kumar, Shameer Khader
Book Image

Healthcare Analytics Made Simple

By: Vikas (Vik) Kumar, Shameer 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)

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...