Artificial Intelligence for Healthcare Applications and Management

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 INDICE: 1. Introduction Boris Galitsky 2. Multi-case-based reasoning by syntactic-semantic alignment and discourse analysis Boris Galitsky 3. Obtaining supported decision trees from text for health system applications Boris Galitsky 4. Search and prevention of errors in medical databases Saveli Goldberg 5. Overcoming AI applications challenges in health: Decision system DINAR2 Saveli Goldberg and Mark Prutkin 6. Formulating critical questions to the user in the course of decision-making Boris Galitsky 7. Relying on discourse analysis to answer complex questions by neural machine reading comprehension Boris Galitsky 8. Machine reading between the lines (RBL) of medical complaints Boris Galitsky 9. Discourse means for maintaining a proper rhetorical flow Boris Galitsky 10. Dialogue management based on forcing a user through a discourse tree of a text Boris Galitsky 11. Building medical ontologies relying on communicative discourse trees Boris Galitsky and Dmitry Ilvovsky 12. Explanation in medical decision support systems Saveli Goldberg 13. Passive decision support for patient management Saveli Goldberg and Stanislav Belyaev 14. Multimodal discourse trees for health management and security Boris Galitsky 15. Improving open domain content generation by text mining and alignment Boris Galitsky

• ISBN: 978-0-12-824521-7
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 548
• Fecha Publicación: 19/01/2022
• Nº Volúmenes: 1
• Idioma: Inglés