Healthcare Analytics Made Simple
Techniques in healthcare computing using machine learning and Python
Seiten
2018
Packt Publishing Limited (Verlag)
978-1-78728-670-2 (ISBN)
Packt Publishing Limited (Verlag)
978-1-78728-670-2 (ISBN)
Machine learning and analytics have been widely utilized across the healthcare sector of late. This book will bridge the gap between practicing doctors and you as a data scientist. You will learn how to work with healthcare data and gain better insight from this data to improve healthcare outcomes.
Add a touch of data analytics to your healthcare systems and get insightful outcomes
Key Features
Perform healthcare analytics with Python and SQL
Build predictive models on real healthcare data with pandas and scikit-learn
Use analytics to improve healthcare performance
Book DescriptionIn 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.
What you will learn
Gain valuable insight into healthcare incentives, finances, and legislation
Discover the connection between machine learning and healthcare processes
Use SQL and Python to analyze data
Measure healthcare quality and provider performance
Identify features and attributes to build successful healthcare models
Build predictive models using real-world healthcare data
Become an expert in predictive modeling with structured clinical data
See what lies ahead for healthcare analytics
Who this book is forHealthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.
Add a touch of data analytics to your healthcare systems and get insightful outcomes
Key Features
Perform healthcare analytics with Python and SQL
Build predictive models on real healthcare data with pandas and scikit-learn
Use analytics to improve healthcare performance
Book DescriptionIn 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.
What you will learn
Gain valuable insight into healthcare incentives, finances, and legislation
Discover the connection between machine learning and healthcare processes
Use SQL and Python to analyze data
Measure healthcare quality and provider performance
Identify features and attributes to build successful healthcare models
Build predictive models using real-world healthcare data
Become an expert in predictive modeling with structured clinical data
See what lies ahead for healthcare analytics
Who this book is forHealthcare Analytics Made Simple is for you if you are a developer who has a working knowledge of Python or a related programming language, although you are new to healthcare or predictive modeling with healthcare data. Clinicians interested in analytics and healthcare computing will also benefit from this book. This book can also serve as a textbook for students enrolled in an introductory course on machine learning for healthcare.
Dr. Vikas (Vik) Kumar grew up in the United States in Niskayuna, New York. He earned his MD from the University of Pittsburgh, but shortly afterwards he discovered his true calling of computers and data science. He then earned his MS in the College of Computing at Georgia Institute of Technology and has subsequently worked as a data scientist for both healthcare and non-healthcare companies. He currently lives in Atlanta, Georgia.
Table of Contents
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
| Erscheinungsdatum | 03.08.2018 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| ISBN-10 | 1-78728-670-3 / 1787286703 |
| ISBN-13 | 978-1-78728-670-2 / 9781787286702 |
| Zustand | Neuware |
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