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)

Summary

In this chapter, we have built a predictive model for predicting outcomes in the emergency department. While there are many machine learning problems in healthcare, this exercise has demonstrated the issues typically faced as one preprocesses healthcare data, trains and scores models, and makes predictions with unlabeled data. This chapter marks the end of the coding portion of this book.

Now that we have seen the construction of a predictive model firsthand, the next logical question to ask is how predictive models have fared when compared with traditional statistical risk scores in predicting clinical outcomes. We explore that question in the next chapter.