Practical Machine Learning for Healthcare Professionals: A Hands-On Beginner’s Guide to Clinical Data, Predictive Models, and Responsible AI

Machine learning is already changing healthcare — but most clinicians were never taught how it works. Practical Machine Learning for Healthcare Professionals is a beginner-friendly, hands-on guide that explains machine learning in clear healthcare terms — without overwhelming math or coding. Whether you work in clinical care, nursing, pharmacy, public health, or hospital operations, this book helps you understand how predictive models are built, evaluated, and responsibly used in real-world settings. What you’ll learn inside: • How machine learning works in healthcare workflows • Clinical data basics (EHR structure, missing values, data quality, leakage) • Predictive models explained simply — from interpretable baselines to modern approaches • Metrics that matter in medicine (sensitivity, specificity, calibration, thresholds) • Bias, fairness, and explainability (how to build trust and reduce harm) • Responsible deployment (monitoring, drift, alert fatigue, governance) Perfect for: • Healthcare professionals curious about AI and machine learning • Healthcare students and trainees building future-ready skills • Hospital teams in quality improvement, analytics, and innovation • Anyone who wants to understand ML results without becoming a data scientist Includes practical tools you can use immediately: • Checklists for data quality, bias, and model safety • Realistic clinical-style scenarios (synthetic examples) • End-of-chapter quizzes, exercises, and workflow walkthroughs • Two capstone projects: Readmission Risk + Early Warning Deterioration Score • A 90-day learning roadmap for continued growth Note: This book is for educational purposes only and does not provide medical advice, diagnosis, or treatment guidance. Build practical ML understanding and communicate confidently in the future of healthcare.