Leveraging Large Language Models for Simplified Patient Summary Generation, Literature Retrieval and Medical Information Summarization: A Health CASCADE Study

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dc.contributor.authorBalaskas, Georgios
dc.contributor.authorPapadopoulos, Homer
dc.contributor.authorKorakis, Antonis
dc.date.accessioned2023-12-26T18:40:03Z
dc.date.available2023-12-26T18:40:03Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2024.394
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherb883ede9-a7bb-418d-b9a5-874e5bec5a74
dc.identifier.urihttps://hdl.handle.net/10125/106777
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Platforms and Ecosystems in Healthcare
dc.subjectelectronic health records
dc.subjectfhir
dc.subjectinformation retrieval
dc.subjectnatural language processing
dc.subjectsummarization
dc.titleLeveraging Large Language Models for Simplified Patient Summary Generation, Literature Retrieval and Medical Information Summarization: A Health CASCADE Study
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractIn the evolving healthcare landscape, integrating advanced technologies such as machine learning and natural language processing has become vital. This paper presents an innovative system that leverages modern Natural Language Processing (NLP) capabilities to extract information from Electronic Health Records (EHRs) and generate simplified patient summaries (SPS). These SPS are subsequently used to provide clinicians with summaries of relevant academic literature, improving their ability to access pertinent information efficiently. The system architecture employs Large Language Models (LLMs) to generate SPSs and summarize relevant information, while dense vector retriever models are used for information retrieval from document corpus, which is created by combining parts of publicly available datasets such as PubMed, the CORD19 dataset, and more. The presented system has the potential to significantly reduce the time and effort required by clinicians to access relevant patient information, allowing them to concentrate more on patient care and contribute to improved patient outcomes.
dcterms.extent10 pages
prism.startingpage3255

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