Artificial Intelligence and Data Mining in Healthcare

Artificial intelligence for real healthcare decisions

Artificial Intelligence and Data Mining in Healthcare is a Springer edited volume that brings together research on how intelligent methods can be used to improve healthcare management and engineering. Rather than treating AI as an abstract trend, the book looks at practical decision-making problems: hospital logistics, patient pathways, capacity planning, operating theater scheduling, image compression, and medical data analysis.

The result is a collection that sits at the intersection of healthcare operations and computational methods. Readers encounter both predictive and prescriptive thinking, with chapters that connect machine learning, optimization, process mining, clustering, and symbolic regression to concrete healthcare questions.

What the book covers

• Healthcare logistics and workflow optimization
• AI/OR synergies for hospital-wide decision support
• Capacity management and patient flow
• Healthcare expenditure and life expectancy analysis
• Operating theater block-scheduling
• Clinical pathway discovery from electronic health records
• Medical image compression for telemedicine
• Feature selection and clustering for medical datasets

Why it stands out

This is a technically oriented volume with a strong applied focus. The chapters are not generic introductions; they are research-driven studies that show how AI and data mining methods can be used to model complex healthcare environments and extract useful structure from medical data. That makes the book especially useful for readers who want substance, not slogans.

Who will find it useful

The editors identify the book as valuable for researchers and master’s and PhD students in computer science, information technology, industrial engineering, and applied mathematics, especially those working in healthcare-related AI and data mining.

For readers building a digital library around healthcare analytics, hospital decision support, or applied machine learning in medicine, this title offers a focused and interdisciplinary collection of current research.