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Healthcare Analytics Made Simple

By : Kumar, Khader

4.4 (8)

Healthcare Analytics Made Simple

4.4 (8)

By: Kumar, 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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

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

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

Machine Learning Foundations

Machine Learning Foundations

Model frameworks for medical decision making

Machine learning pipeline

Summary

References and further reading

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

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

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

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

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

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

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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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