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

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.

Dr. 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.