TY  - BOOK
AU  - Olson,David L.
AU  - Araz,Özgür M.
TI  - Data Mining and Analytics in Healthcare Management: Applications and Tools 
T2  - International Series in Operations Research & Management Science,
SN  - 9783031281136
U1  - 362.1068 23
PY  - 2023///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Health services administration
KW  - Production management
KW  - Business
KW  - Data processing
KW  - Data mining
KW  - Medical informatics
KW  - Health Care Management
KW  - Operations Management
KW  - Business Analytics
KW  - Data Mining and Knowledge Discovery
KW  - Health Informatics
N1  - Chapter 1: Urgency in Healthcare Data Analytics -- Chapter 2: Analytics and Knowledge Management in Healthcare -- Chapter 3: Visualization -- Chapter 4: Association Rules -- Chapter 5: Cluster Analysis -- Chapter 6: Time Series Forecasting -- Chapter 7: Classification Models -- Chapter 8: Applications of Predictive Data Mining in Healthcare -- Chapter 9: Decision Analysis and Applications in Healthcare -- Chapter 10: Analysis of Four Medical Datasets -- Chapter 11: Multiple Criteria Decision Models in Healthcare- Chapter 12: Naïve Bayes Models in Healthcare -- Chapter 13: Summation
N2  - This book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today's world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector
UR  - https://doi.org/10.1007/978-3-031-28113-6
ER  -