https://arxiv.org/abs/2403.17199 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2403.17199 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2403.17199 (cs) [Submitted on 25 Mar 2024] Title:Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model Authors:Braja Gopal Patra, Lauren A. Lepow, Praneet Kasi Reddy Jagadeesh Kumar, Veer Vekaria, Mohit Manoj Sharma, Prakash Adekkanattu, Brian Fennessy, Gavin Hynes, Isotta Landi, Jorge A. Sanchez-Ruiz, Euijung Ryu, Joanna M. Biernacka, Girish N. Nadkarni, Ardesheer Talati, Myrna Weissman, Mark Olfson, J. John Mann, Alexander W. Charney, Jyotishman Pathak View a PDF of the paper titled Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model, by Braja Gopal Patra and 18 other authors View PDF HTML (experimental) Abstract:Background: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented as narrative clinical notes rather than structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of data extraction. Data and Methods: Psychiatric encounter notes from Mount Sinai Health System (MSHS, n=300) and Weill Cornell Medicine (WCM, n= 225) were annotated and established a gold standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (e.g., social network, instrumental support, and loneliness). Results: For extracting SS/SI, the RBS obtained higher macro-averaged f-scores than the LLM at both MSHS (0.89 vs. 0.65) and WCM (0.85 vs. 0.82). For extracting subcategories, the RBS also outperformed the LLM at both MSHS (0.90 vs. 0.62) and WCM (0.82 vs. 0.81). Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across all metrics. Intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS were designed and refined to follow the same specific rules as the gold standard annotations. Conversely, the LLM were more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages and are made available open-source for future testing. Comments: 2 figures, 3 tables Subjects: Computation and Language (cs.CL) Cite as: arXiv:2403.17199 [cs.CL] (or arXiv:2403.17199v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2403.17199 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Braja Gopal Patra [view email] [v1] Mon, 25 Mar 2024 21:19:50 UTC (402 KB) Full-text links: Access Paper: View a PDF of the paper titled Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model, by Braja Gopal Patra and 18 other authors * View PDF * HTML (experimental) * TeX Source * Other Formats license icon view license Current browse context: cs.CL < prev | next > new | recent | 2403 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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