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OverviewHealthcare Applications of Neuro-Symbolic Artificial Intelligence provides a comprehensive introduction to the field of neuro-symbolic (NS) artificial intelligence (AI), presenting the most recent advances in deep learning and integration of NS systems and large language models (LLMs). This book evaluates traditional approaches, current approaches, as well as the author’s own approach to NS, to create hybrid architectures and reasoning techniques to overcome the limitations of most existing AI systems such as deep learning, neural networks, and symbolic AI. This book will be a welcome resource for researchers and graduate students in AI, natural language processing, and biomedical informatics, as well as professionals in software development looking to redesign current systems to leverage LLMs through the health application of NS architecture. Full Product DetailsAuthor: Boris Galitsky (Knowledge-Trail, Inc., San Jose, CA, USA)Publisher: Elsevier Science Publishing Co Inc Imprint: Academic Press Inc Weight: 0.450kg ISBN: 9780443300462ISBN 10: 0443300461 Pages: 300 Publication Date: 01 August 2025 Audience: College/higher education , Professional and scholarly , Postgraduate, Research & Scholarly , Professional & Vocational Format: Paperback Publisher's Status: Forthcoming Availability: Not yet available ![]() This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release. Table of Contents1. Neuro-Symbolic Shaped-Charge Learning Architecture 2. Health Applications of Shaped-Charge Learning 3. Enabling LLM with plug-and-play symbolic reasoning components 4. Extending LLM capabilities beyond reasoning (Boris Galitsky and Alexander Rybalov) 5. Differential Diagnose-making with LLM and Probabilistic Logic Program 6. LLM-based Personalized Recommendations in Health 7. Leveraging Medical Discourse to Answer Complex Questions 8. Identifying LLM Hallucinations in Health Communication 9. Enabling LLMs with explainability 10. Explainability Discourse 11. Enabling Retrieval-Augmented Generation and Knowledge Graphs with Discourse Analysis 12. Employing LLM to solve Constraint Satisfaction 13. Kolmogorov-Arnold Network for Word-Level Explainable Meaning Representation 14. ConclusionsReviewsAuthor InformationDr. 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. Tab Content 6Author Website:Countries AvailableAll regions |