Artificial Intelligence for Healthcare Applications and Management
- 1st Edition - January 13, 2022
- Imprint: Academic Press
- Authors: Boris Galitsky, Saveli Goldberg
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 4 5 2 1 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 4 5 2 2 - 4
Artificial Intelligence for Healthcare Applications and Management introduces application domains of various AI algorithms across healthcare management. Instead of discussing AI fi… Read more
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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
Researchers, professionals, and graduate students in computer science and engineering, bioinformatics, medical informatics, and biomedical and clinical engineering.
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
- Edition: 1
- Published: January 13, 2022
- Imprint: Academic Press
- Language: English
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Boris Galitsky
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.
Affiliations and expertise
Knowledge-Trail, Inc., San Jose, CA, USASG
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.
Affiliations and expertise
Division of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USARead Artificial Intelligence for Healthcare Applications and Management on ScienceDirect