000			02983cam a2200289zu 4500
001			88930558
003			FRCYB88930558
005			20250107235621.0
006			m o d
007			cr un
008			250108s2022 fr | o|||||0|0|||eng d
020			_a9780128245217
035			_aFRCYB88930558
040			_aFR-PaCSA _ben _c _erda
100	1		_aGalitsky, Boris
245	0	1	_aArtificial Intelligence for Healthcare Applications and Management _c['Galitsky, Boris', 'Goldberg, Saveli']
264		1	_bElsevier Science _c2022
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aGalitsky, Boris
700	0		_aGoldberg, Saveli
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88930558 _qtext/html _a
520			_aArtificial 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
999			_c75161 _d75161