The Power of Prediction in Health Care: A Step-by-step Guide to Data Science in Health Care
Description
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.
More details
Person
Content
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
