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

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  • Formaat: Paperback / softback, 195 pages, kõrgus x laius: 235x155 mm, kaal: 338 g, 39 Illustrations, color; 23 Illustrations, black and white; XIX, 195 p. 62 illus., 39 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 26-Jan-2022
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030452425
  • ISBN-13: 9783030452421
Teised raamatud teemal:
  • Pehme köide
  • Hind: 141,35 €*
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  • Tavahind: 166,29 €
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  • Formaat: Paperback / softback, 195 pages, kõrgus x laius: 235x155 mm, kaal: 338 g, 39 Illustrations, color; 23 Illustrations, black and white; XIX, 195 p. 62 illus., 39 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 26-Jan-2022
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030452425
  • ISBN-13: 9783030452421
Teised raamatud teemal:

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.