The Power of Prediction in Health Care: A Step-by-step Guide to Data Science in Health Care: A Step-by-step Guide to Data Science in Health Care

by Rafiq Muhammad

Overview

Are you an aspiring data science student or early career researcher taking your first steps into data science? Are you overwhelmed and lost in the vast sea of information? This simplified data science guide is for you.

This book provides a step-by-step approach to how data science projects can be conceptualized, designed, and developed in health care by aspiring data scientists. We will start on an educational journey that equips graduate students and early career researchers with hands-on knowledge and practical skills so they may fully realize the amazing potential of data science in healthcare.

The book provides:

• step-by-step approach to designing and developing data science projects in healthcare
• easy-to-understand structure to facilitate the development of data science projects for beginners
• links to useful resources and tools (mostly free and open source) that help build and execute AI projects in healthcare
• links to free-to-use healthcare databases
• Data science case study examples that demonstrate how to build data science projects

Whether you are a healthcare professional looking to enhance your skills or a data scientist seeking to work in the healthcare industry, "The Power of Prediction in Health Care" is an essential guide to unlocking the potential of data science in healthcare.

With real-world examples and practical advice, this book will empower you to make data-driven decisions that improve patient outcomes and transform healthcare.

Product Details

ISBN-13:	9789198900804
Publisher:	Muhammad Rafiq
Publication date:	04/17/2024
Sold by:	Barnes & Noble
Format:	eBook
Pages:	181
File size:	1 MB

About the Author

Rafiq Muhammad has background in healthcare with MBA from SDA Bocconi School of Management, Italy, and holds a Ph.D. in Artificial Intelligence and Machine Learning from Karolinska Institute, Sweden. He has spent the past several years engaging deeply in data analytics and machine learning applications.Rafiq Muhammad is passionate about data science and artificial intelligence and is dedicated to bridging the gap between cutting-edge generative language models and their practical implementations in real life. Rafiq Muhammad has extensive experience in publishing systematic literature reviews in peer-reviewed journals, and teaching and supervising graduate students on how to conduct literature reviews. He also has experience teaching Master Level courses at Karolinska Institute. Rafiq has developed advanced skills in conducting high-quality systematic literature reviews independently (four systematic literature reviews already published and supervised several). During the last 6 years, He has published 10 scientific articles in peer-reviewed academic journals. He has spent the past several years immersing himself in data analytics using artificial intelligence and machine learning, conducting literature reviews, and teaching master-level courses in healthcare. As a medical doctor with expertise in data science, he has the right competence to conceptualize, design, and develop AI applications in healthcare.

Table of Contents

Why I Wrote This Book?5

Unique Features and Structure of This Book8

Target Audience of This Book10

About The Author11

Table of Contents13

Chapter 1. Introduction17

Definition of AI and Data Science in Healthcare18

Historical Perspective of AI in Healthcare22

Importance of AI and Data Science in Healthcare26

Career in Data Science and Artificial Intelligence in Healthcare28

Benefits of Career in Data Science and Artificial Intelligence in Healthcare29

Chapter 2. Fundamentals of Data Science in Healthcare31

Data Collection and Integration32

Types of Data35

Data Sources38

Data Quality40

Data Collection42

Data Preprocessing and Cleaning43

Data Exploration and Visualization47

Predictive Modeling51

Machine Learning Algorithms51

Feature Selection and Engineering51

Ethical Considerations and Privacy52

Interpretability and Explainability52

Validation and Evaluation52

Clinical Integration and Decision Support53

Continuous Learning and Improvement53

Types of AI54

Types of Machine learning algorithms56

Performance Metrics and Evaluation Methods59

Chapter 3. Steps in Data Analysis and AI Model Development66

Problem Definition66

Data Collection and Data Cleaning67

Exploratory Data Analysis68

Feature Selection and Feature Engineering68

Data Splitting69

Model Selection70

Model Development70

Model Evaluation71

Model Interpretation71

Model Deployment72

Model Monitoring and Maintenance72

Ethical Considerations73

Documentation73

Chapter 4. Tools and Resources for Healthcare Data Science74

ChatGPT-Assisted Data Science74

Free Datasets for Healthcare Data Science77

Programming Languages81

Data Visualization Tools86

Machine Learning Frameworks90

Big Data Tools92

Online AI and ML Tools94

Healthcare Data Standards96

Chapter 5. Case study of Hospital Readmission Prediction with R98

Chapter 6. Applications of AI and Data Science in Clinical Decision Making138

Clinical Decision Support Systems138

Diagnostic Imaging and Radiology139

Precision Medicine and Genomics140

Mental Health141

AI in Clinical Trials, Drug Discovery and Development142

Electronic Health Records and Clinical Workflows143

Chapter 7. Applications of AI and Data Science in Healthcare Operations144

Telemedicine and Remote Patient Care144

Healthcare Supply Chain and Logistics145

Fraud Detection and Prevention146

Disease Surveillance/Public Health147

Chapter 8. Ethical Considerations and Challenges148

Bias and Fairness in AI Models148

Privacy and Data Security149

Impact on Healthcare Workforce151

Legal and Regulatory Issues154

Patient Safety and Healthcare Quality157

Chapter 9. Future Directions and Challenges159

One Last Thing163

10. References164