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Artificial Intelligence and Data Mining in Healthcare 2021 ed. [Kõva köide]

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  • Formaat: Hardback, 195 pages, kõrgus x laius: 235x155 mm, kaal: 494 g, 39 Illustrations, color; 23 Illustrations, black and white; XIX, 195 p. 62 illus., 39 illus. in color., 1 Hardback
  • Ilmumisaeg: 26-Jan-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030452395
  • ISBN-13: 9783030452391
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  • Kõva köide
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Artificial Intelligence and Data Mining in Healthcare 2021 ed.Suurem pilt
  • Formaat: Hardback, 195 pages, kõrgus x laius: 235x155 mm, kaal: 494 g, 39 Illustrations, color; 23 Illustrations, black and white; XIX, 195 p. 62 illus., 39 illus. in color., 1 Hardback
  • Ilmumisaeg: 26-Jan-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030452395
  • ISBN-13: 9783030452391
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030452391.html
Märksõnad:

This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection.

The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.


Artificial Intelligence for Healthcare Logistics: An Overview and Research Agenda.- Synergy Between Predictive Mining and Prescriptive Planning of Complex Patient Pathways Considering Process Discrepancies for Effective Hospital-Wide Decision Support.- Real-Time Capacity Management and Patient Flow Optimization in Hospitals Using AI Methods.- How Healthcare Expenditure Influences Life Expectancy: Case Study on Russian Regions.- Operating Theater Management System: Block-Scheduling.- An Immune Memory and a Negative Selection to Visualize Clinical Pathways from Electronic Health Records.- Optimized Medical Images Compression for Telemedicine Applications.- Online Variational Learning Using Finite Generalized Inverted Dirichlet Mixture Model with Feature Selection on Medical Data Sets.- Entropy-Based Variational Inference for Semi-bounded Data Clustering in Medical Applications.