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)

Standardized clinical codesets

Being philosophical for a moment, every known object that has a significant importance attributed to it has a name. The organs you are using to read these words are known as eyes. The words are written on pieces of paper called pages. To turn the pages, you use your hands. These are all objects that we have named so that we can identify them easily.

In healthcare, important entities—diseases, procedures, lab tests, drugs, symptoms, bacteria species, for example, have names and identities too. For example, the failure of the heart valves to pump blood to the rest of the body is known as heart failure. ACE inhibitors are a class of drugs used to treat heart failure.

A problem arises, however, when healthcare industry workers associate the same entity with different identities. For example, one physician may refer to "heart failure&quot...