Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
In 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.
Recommended Citation
Balaskas, Georgios; Papadopoulos, Homer; and Korakis, Antonis, "Leveraging Large Language Models for Simplified Patient Summary Generation, Literature Retrieval and Medical Information Summarization: A Health CASCADE Study" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 4.
https://aisel.aisnet.org/hicss-57/hc/ecosystems/4
Leveraging Large Language Models for Simplified Patient Summary Generation, Literature Retrieval and Medical Information Summarization: A Health CASCADE Study
Hilton Hawaiian Village, Honolulu, Hawaii
In 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.
https://aisel.aisnet.org/hicss-57/hc/ecosystems/4