Boris Galitsky, Saveli Goldberg

Artificial Intelligence for Healthcare Applications and Management (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 548
• Erscheinungstermin: 13. Januar 2022
• Englisch
• ISBN-13: 9780128245224
• Artikelnr.: 68422514

Autorenporträt

Dr. Boris Galitsky has contributed linguistic and machine learning technologies to Silicon Valley startups as well as companies like eBay and Oracle for over 25 years. Dr. Galitsky's information extraction and sentiment analysis techniques assisted several acquisitions, such as Xoopit by Yahoo, Uptake by Groupon, Log logic by Tibco and Zvents by eBay. His security-related technologies of document analysis contributed to the acquisition of Elastica by Semantec. As an architect of the Intelligent Bots project at Oracle, Dr. Galitsky developed a discourse analysis technique used for dialogue management and published in the book "Developing Enterprise Chatbots. He also published a two-volume monograph "AI for CRM, based on his experience developing Oracle Digital Assistant. He is Apache committer to OpenNLP where he created OpenNLP. Similarity component which is a basis for a semantically enriched search engine and chatbot development. Dr. Galitsky's exploration and formalization of human reasoning culminated in the book "Computational Autism broadly used by parents of children with autistic reasoning and rehabilitation personnel. His focus on the medical domain led to another research monograph, "Artificial Intelligence for Healthcare Applications and Management, co-authored with Dr. Saveli GoldbergDr. Saveli Goldberg has contributed biostatistics and machine learning technologies to research at Harvard Medical School and Massachusetts General Hospital for the last 20 years, where he is currently a biostatistician and data analyst. The author of more than 80 publications and 2 patents, he is currently researching several projects in the field of radiation oncology and endocrinology. The main areas of his research include (a) optimal strategies in cancer radiation therapy, (b) optimal targets and strategies in the treatment of diabetes and hypertension, (c) the optimal combination of expert and artificial intelligence to get the right solution, (d) explanation of the machine learning solution, and (e) the relationship of electronic documentation to patient outcomes.

Inhaltsangabe

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