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)

Readmission prediction

Predicting the likelihood of all-cause patient readmissions is outside the scope of a typical clinician's knowledge base, since it is not tied to a specific organ system or disease. However, it is becoming a problem of increasing importance in the healthcare world, since preventable hospital readmissions are a major cause of elevated healthcare expenditures in the United States and other countries. We discussed the incentive and rationale for predicting hospital readmissions and the US government's Hospital Readmission Reduction Program (HRRP) in Chapter 6, Measuring Healthcare Quality. Let's now review how machine learning algorithms can be used to augment simpler readmission risk scores.

LACE and HOSPITAL scores

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