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)

Case details – predicting mortality for a cardiology practice

The cardiology practice you are working with has two physicians on staff: Dr. Johnson and Dr. Wu. While the practice has many patients, they are interested in identifying which patients who visit are at high risk of all-cause mortality within the next 6 months. Having an outpatient visit sometime in 2016 makes up the inclusion criteria for the analytics. The target variable is whether the patient passed away within 6 months of their visit.

Now that we've reviewed the details of the modeling assignment, let's take a look at the five patients in the database. The preliminary data sent to you by the cardiology practice includes information on five patients, distributed across six tables. The following are case vignettes for each of the patients. Note that this section is heavy on clinical terminology related...