Artificial Intelligence for Healthcare Applications and Management - Tapa blanda

Acerca de los autores

Dr. Boris Galitsky is a cofounder of Knowledge Trail, San Jose, CA. He has contributed linguistic and machine learning technologies to Silicon Valley start-ups as well as companies such as eBay and Oracle for over 25 years. His information extraction and sentiment analysis techniques assisted several acquisitions, such as Xoopit by Yahoo, Uptake by Groupon, LogLogic by Tibco, and Zvents by eBay. His security-related technologies of document analysis contributed to the acquisition of Elastica by Symantec. As an architect of the Intelligent Bots project at Oracle, he 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 an Apache committer to OpenNLP where he created OpenNLP. Similarity component that 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 autism 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 Goldberg.

De la contraportada

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.