Artificial Intelligence for Healthcare Applications and Management (notice n° 75161)

détails MARC
000 -LEADER
fixed length control field 02983cam a2200289zu 4500
003 - CONTROL NUMBER IDENTIFIER
control field FRCYB88930558
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250107235621.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250108s2022 fr | o|||||0|0|||eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780128245217
035 ## - SYSTEM CONTROL NUMBER
System control number FRCYB88930558
040 ## - CATALOGING SOURCE
Original cataloging agency FR-PaCSA
Language of cataloging en
Transcribing agency
Description conventions rda
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Galitsky, Boris
245 01 - TITLE STATEMENT
Title Artificial Intelligence for Healthcare Applications and Management
Statement of responsibility, etc. ['Galitsky, Boris', 'Goldberg, Saveli']
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer Elsevier Science
Date of production, publication, distribution, manufacture, or copyright notice 2022
300 ## - PHYSICAL DESCRIPTION
Extent p.
336 ## - CONTENT TYPE
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type code c
Source rdamdedia
338 ## - CARRIER TYPE
Carrier type code c
Source rdacarrier
520 ## - SUMMARY, ETC.
Summary, etc. Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI first and then exploring its applications in healthcare afterward, the authors attack the problems in context directly, in order to accelerate the path of an interested reader toward building industrial-strength healthcare applications. Readers will be introduced to a wide spectrum of AI applications supporting all stages of patient flow in a healthcare facility. The authors explain how AI supports patients throughout a healthcare facility, including diagnosis and treatment recommendations needed to get patients from the point of admission to the point of discharge while maintaining quality, patient safety, and patient/provider satisfaction. AI methods are expected to decrease the burden on physicians, improve the quality of patient care, and decrease overall treatment costs. Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. This book focuses on predictive analytics, health text processing, data aggregation, management of patients, and other fields which have all turned out to be bottlenecks for the efficient management of coronavirus patients. Presents an in-depth exploration of how AI algorithms embedded in scheduling, prediction, automated support, personalization, and diagnostics can improve the efficiency of patient treatment Investigates explainable AI, including explainable decision support and machine learning, from limited data to back-up clinical decisions, and data analysis Offers hands-on skills to computer science and medical informatics students to aid them in designing intelligent systems for healthcare Informs a broad, multidisciplinary audience about a multitude of applications of machine learning and linguistics across various healthcare fields Introduces medical discourse analysis for a high-level representation of health texts
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Galitsky, Boris
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Goldberg, Saveli
856 40 - ELECTRONIC LOCATION AND ACCESS
Access method Cyberlibris
Uniform Resource Identifier <a href="https://international.scholarvox.com/netsen/book/88930558">https://international.scholarvox.com/netsen/book/88930558</a>
Electronic format type text/html
Host name

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