02641cam a22003618i 4500 19631 19631 22753750 OSt 20240801170911.0 220816s2023 flu b 001 0 eng 2022037119 9781032208329 (pbk) DLC eng rda DLC pcc 610.285 63 SHA 23/eng/20230123 Machine learning and artificial intelligence in healthcare systems : tools and techniques / [edited by] Tawseef Ayoub Shaikh, Saqib Hakak, Tabasum Rasool. First edition. 2302 Boca Raton, FL : CRC Press, 2023. vi, 350 pages : illustrations ; 25 cm. text txt rdacontent unmediated n rdamedia volume nc rdacarrier Includes bibliographical references and index. "This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine by combining design and problem-solving skills of engineering with health sciences to advance healthcare treatment. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques discusses AI-based smart paradigms for reliable prediction of infectious disease dynamics which can help or prevent disease transmission. It highlights the different aspects of using extended reality for diverse healthcare applications and aggregates the current state of research. The book offers intelligent models of the smart recommender system for personal well-being services and computer-aided drug discovery and design methods. Case studies illustrating the business processes that underline the use of big data and health analytics to improve healthcare delivery are center stage. Innovative techniques used for extracting user social behavior known as sentiment analysis for healthcare-related purposes round out the diverse array of topics this reference book covers. Contributions from experts in the field, this book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering"-- Provided by publisher. Artificial intelligence Medical applications. Machine learning. Shaikh, Tawseef Ayoub, editor. Hakak, Saqib, editor. Rasool, Tabasum, editor. 7 cbc orignew 1 ecip 20 y-gencatlg ddc RFT 0 0 ddc 0 0 R-22/2 AIISH AIISH 2024-08-01 0 610.285 63 SHA 23867 2024-08-01 00:00:00 2024-08-01 RFT