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Artificial Intelligence for Healthcare Applications and Management
1st Edition - January 13, 2022
Authors: Boris Galitsky, Saveli Goldberg
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… 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.
- Cover
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1: Introduction
- Abstract
- Acknowledgments
- Supplementary data sets
- 1: The issues of ML in medicine this book is solving
- 2: AI for diagnosis and treatment
- 3: Health discourse
- References
- Chapter 2: Multi-case-based reasoning by syntactic-semantic alignment and discourse analysis
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Multi-case-based reasoning in the medical field
- 3: Alignment of linguistic graphs
- 4: Case-based reasoning in health
- 5: Building a repository of labeled cases and diagnoses
- 6: System architecture
- 7: Evaluation
- 8: Related work
- 9: Conclusions
- References
- Chapter 3: Obtaining supported decision trees from text for health system applications
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Obtaining supported decision trees from text
- 3: Evaluation
- 4: Decision trees in health
- 5: Expert system for health management
- 6: Conclusions
- References
- Chapter 4: Search and prevention of errors in medical databases
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Data entry errors when transferring information from the initial medical documentation to the studied database
- 3: Errors in initial medical information
- 4: Error reduction
- 5: Conclusions
- References
- Chapter 5: Overcoming AI applications challenges in health: Decision system DINAR2
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Problems of introducing medical AI applications
- 3: Integrated decision support system at the regional consultative Center for Intensive Pediatrics (DINAR2)
- 4: Conclusions
- References
- Chapter 6: Formulating critical questions to the user in the course of decision-making
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Reasoning patterns and formulating critical questions
- 3: Automated building of reasoning chains
- 4: Question-generation system architecture
- 5: Evaluation
- 6: Syntactic and semantic generalizations
- 7: Building questions via generalization of instances
- 8: Discussion and conclusions
- References
- Chapter 7: Relying on discourse analysis to answer complex questions by neural machine reading comprehension
- Abstract
- 1: Introduction
- 2: Examples where discourse analysis is essential for MRC
- 3: Discourse dataset
- 4: Discourse parsing
- 5: Incorporating syntax into model
- 6: Attention mechanism for the sequence of tokens
- 7: Enabling attention mechanism with syntactic features
- 8: Including discourse structure into the model
- 9: Pre-trained language models and their semantic extensions
- 10: Direct similarity-based question answering
- 11: System architecture
- 12: Evaluation
- 13: Discussion and conclusions
- Supplementary data sets
- References
- Chapter 8: Machine reading between the lines (RBL) of medical complaints
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: RBL as generalization and web mining
- 3: System architecture
- 4: Statistical model of RBL
- 5: RBL and NLI
- 6: Evaluation
- 7: Discussions
- 8: Conclusions
- References
- Chapter 9: Discourse means for maintaining a proper rhetorical flow
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Discourse tree of a dialogue
- 3: Computing rhetorical relation of entailment
- 4: Dialogue generation as language modeling
- 5: Rhetorical agreement between questions and answers
- 6: Discourse parsing of a dialogue
- 7: Constructing a dialogue from text
- 8: System architecture
- 9: Evaluation
- 10: Discussions and conclusions
- References
- Chapter 10: Dialogue management based on forcing a user through a discourse tree of a text
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Keeping a learner focused on a text
- 3: Navigating discourse tree in conversation
- 4: The dialogue flow
- 5: User intent recognizer
- 6: System architecture
- 7: Evaluation
- 8: Related work
- 9: Conclusions
- References
- Chapter 11: Building medical ontologies relying on communicative discourse trees
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Introducing discourse features
- 3: Informative and uninformative parts of text
- 4: Designing ontologies
- 5: Neural dictionary manager
- 6: Phrase aggregator
- 7: Ontologies supporting reasoning
- 8: Specific ontology types in bioinformatics
- 9: Supporting search
- 10: System architecture
- 11: Evaluation
- 12: Conclusions
- References
- Chapter 12: Explanation in medical decision support systems
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Models of machine learning explanation
- 3: Explanation based on comparison of the local case with the closest case with an alternative ML solution
- 4: A bi-directional adversarial meta-agent between user and ML system
- 5: Discussion
- 6: Conclusions
- References
- Chapter 13: Passive decision support for patient management
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Dr. Watson-type systems
- 3: Patient management system (SAGe)
- 4: Conclusions
- References
- Chapter 14: Multimodal discourse trees for health management and security
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Discourse analysis of health and security-related scenarios
- 3: Multimodal discourse representation
- 4: Mobile location data and COVID-19
- 5: Reasoning about a cause and effect of data records
- 6: System architecture
- 7: Evaluation
- 8: Discussions and conclusions
- References
- Chapter 15: Improving open domain content generation by text mining and alignment
- Abstract
- Supplementary data sets
- 1: Introduction
- 2: Processing raw natural language generation results
- 3: Fact-checking of deep learning generation
- 4: System architecture
- 5: Probabilistic text merging
- 6: Graph-based fact-checking
- 7: Entity substitution
- 8: Evaluation
- 9: Discussions
- 10: Conclusions
- References
- Index
- No. of pages: 548
- Language: English
- Published: January 13, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780128245217
- eBook ISBN: 9780128245224
BG
Boris Galitsky
Dr. Boris Galitsky contributed linguistic and machine learning technologies to Silicon Valley startups as well as companies like eBay and Oracle for over 25 years. Boris’ information extraction and sentiment analysis techniques assisted a number of 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 acquisition of Elastica by Semantec.
As an architect of the Intelligent Bots project at Oracle, Boris developed a discourse analysis technique user 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. Boris is Apache committer to OpenNLP where he created OpenNLP. Similarity component which is a basis for a semantically-enriched search engine and chatbot development.
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. Boris’ focus on medical domain led to another research monograph, “AI for Health Applications and Management.”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, USA