For this chapter, let's pretend you secured a predictive analytics assignment with a cardiology practice located in the United States. The practice wants you to predict which patients are at risk of dying within 6 months of their visit to the clinic. They make their data available to you in the form of a database that includes six tables. For simplicity, we truncate the database to include the information for five patients only. Our task is to manipulate the data using the SQL language to consolidate it into a single table so that it can be used for machine learning. We will first go over the patients in the database and the database structure. Then, we will introduce basic SQL concepts for engineering and manipulate the data into a form amenable to machine learning.
Healthcare Analytics Made Simple
By :
Healthcare Analytics Made Simple
By:
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.
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Current Title:
Healthcare Analytics Made Simple
Table of Contents (11 chapters)
Preface
Free Chapter
Introduction to Healthcare Analytics
Healthcare Foundations
Machine Learning Foundations
Computing Foundations – Databases
Computing Foundations – Introduction to Python
Measuring Healthcare Quality
Making Predictive Models in Healthcare
Healthcare Predictive Models – A Review
The Future – Healthcare and Emerging Technologies
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