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.

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Table of Contents (11 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to Healthcare Analytics
Introduction to Healthcare Analytics
What is healthcare analytics?
Foundations of healthcare analytics
History of healthcare analytics
Examples of healthcare analytics
Exploring the software
Summary
References
2
Healthcare Foundations
Healthcare Foundations
Healthcare delivery in the US
Patient data – the journey from patient to computer
Standardized clinical codesets
Breaking down healthcare analytics
Summary
References and further reading
3
Machine Learning Foundations
Machine Learning Foundations
Model frameworks for medical decision making
Machine learning pipeline
Summary
References and further reading
4
Computing Foundations – Databases
Computing Foundations – Databases
Introduction to databases
Data engineering with SQL – an example case
Case details – predicting mortality for a cardiology practice
Starting an SQLite session
Data engineering, one table at a time with SQL
Summary
References and further reading
5
Computing Foundations – Introduction to Python
Computing Foundations – Introduction to Python
Variables and types
Data structures and containers
Programming in Python – an illustrative example
Introduction to pandas
Introduction to scikit-learn
Additional analytics libraries
Summary
6
Measuring Healthcare Quality
Measuring Healthcare Quality
Introduction to healthcare measures
US Medicare value-based programs
The Hospital Value-Based Purchasing (HVBP) program
The Hospital Readmission Reduction (HRR) program
The Hospital-Acquired Conditions (HAC) program
The End-Stage Renal Disease (ESRD) quality incentive program
The Skilled Nursing Facility Value-Based Program (SNFVBP)
The Home Health Value-Based Program (HHVBP)
The Merit-Based Incentive Payment System (MIPS)
Other value-based programs
Comparing dialysis facilities using Python
Comparing hospitals
Summary
References
7
Making Predictive Models in Healthcare
Making Predictive Models in Healthcare
Introduction to predictive analytics in healthcare
Our modeling task – predicting discharge statuses for ED patients
Obtaining the dataset
Starting a Jupyter session
Importing the dataset
Making the response variable
Splitting the data into train and test sets
Preprocessing the predictor variables
Final preprocessing steps
Building the models
Using the models to make predictions
Improving our models
Summary
References and further reading
8
Healthcare Predictive Models – A Review
Healthcare Predictive Models – A Review
Predictive healthcare analytics – state of the art
Overall cardiovascular risk
Congestive heart failure
Cancer
Readmission prediction
Other conditions and events
Summary
References and further reading
9
The Future – Healthcare and Emerging Technologies
The Future – Healthcare and Emerging Technologies
Healthcare analytics and the internet
Healthcare and deep learning
Obstacles, ethical issues, and limitations
Conclusion of this book
References and further reading
10
Other Books You May Enjoy
Other Books You May Enjoy
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References and further reading

Ayer T, Alagoz O, Chhatwal J, Shavlik JW, Kahn Jr. CE, Burnside ES (2010). Breast Cancer Risk Estimation with Artificial Neural Networks Revisited: Discrimination and Calibration. Cancer 116 (14): 3310-3321.

Baines CJ (1989). Breast self-examination. Cancer 64(12 Suppl): 2661-2663.

Cruz JA, Wishart DS (2006). Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Informatics 2: 59-77.

D'Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB (2008). General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study. Circulation 117 (6): 743-753.

Donze J, Aujesky D, Williams D, Schnipper JL (2013). Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med 173(8): 632-638.

Donze JD, Williams MV, Robinson...

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