Learning Resource and Development
Amazon cover image
Image from Amazon.com
Image from Coce

Artificial intelligence for healthcare applications and management / Boris Galitsky and Saveli Goldberg.

By: Contributor(s): Material type: TextTextPublisher: London, United Kingdom ; San Diego, California : Academic Press, an imprint of Elsevier, 2022Description: xv, 532 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780128245217
Subject(s): DDC classification:
  • 610.28563 G134a
LOC classification:
  • R859.7.A78 .G35 2022
Contents:
Introduction -- Multi-case-based reasoning by syntactic-semantic alignment and discourse analysis -- Obtaining supported decision trees from text for health system applications -- Search and prevention of errors in medical databases -- Overcoming AI applications challenges in health: Decision system DINAR2 -- Formulating critical questions to the user in the course of decision-making -- Relying on discourse analysis to answer complex questions by neural machine reading comprehension -- Machine reading between the lines (RBL) of medical complaints -- Discourse means for maintaining a proper rhetorical flow -- Dialogue management based on forcing a user through a discourse tree of a text -- Building medical ontologies relying on communicative discourse trees -- Explanation in medical decision support systems -- Passive decision support for patient management -- Multimodal discourse trees for health management and security -- Improving open domain content generation by text mining and alignment.
Summary: " 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." -- Back cover
Tags from this library: No tags from this library for this title. Log in to add tags.
Holdings
Item type Current library Shelving location Call number Copy number Status Date due Barcode
Books Books Main Library Graduate School Library GRD 610.28563 G134a 2022 (Browse shelf(Opens below)) 1-2 Available 030284
Books Books Main Library Graduate School Library GRD 610.28563 G134a 2022 (Browse shelf(Opens below)) 2-2 Available 030285

Includes bibliographical references and index.

Introduction -- Multi-case-based reasoning by syntactic-semantic alignment and discourse analysis -- Obtaining supported decision trees from text for health system applications -- Search and prevention of errors in medical databases -- Overcoming AI applications challenges in health: Decision system DINAR2 -- Formulating critical questions to the user in the course of decision-making -- Relying on discourse analysis to answer complex questions by neural machine reading comprehension -- Machine reading between the lines (RBL) of medical complaints -- Discourse means for maintaining a proper rhetorical flow -- Dialogue management based on forcing a user through a discourse tree of a text -- Building medical ontologies relying on communicative discourse trees -- Explanation in medical decision support systems -- Passive decision support for patient management -- Multimodal discourse trees for health management and security -- Improving open domain content generation by text mining and alignment.

"
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." -- Back cover

There are no comments on this title.

to post a comment.