{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T07:04:09Z","timestamp":1787987049271,"version":"build-2784847793"},"reference-count":23,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"9","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Clin Cancer Inform"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec>\n                    <jats:title>PURPOSE<\/jats:title>\n                    <jats:p>Social Determinants of Health (SDoH) have a significant effect on health outcomes and inequalities. SDoH can be extracted from electronic health records (EHR) to aid policy development and research to improve population health. Automated extraction using artificial intelligence (AI) can improve efficiency and cost-effectiveness. The focus of this study was to autonomously extract comprehensive SDoH details from EHR using a natural language processing (NLP)\u2013based AI pipeline.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>MATERIALS AND METHODS<\/jats:title>\n                    <jats:p>A curated set of 1,000 BC Cancer clinical documents with concentrated SDoH information served as the reference standard for training and evaluating NLP models. Two pipelines were used: an open-source pipeline trained on the annotated medical documents and an industrial pretrained solution used as a benchmark. Three experiments optimized the first pipeline's performance, assessing the effect of including subtype word positions during training. The superior open-source pipeline was then used to extract SDoH information from 13,258 oncology documents.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>The open-source pipeline achieved an average F1 score accuracy of 0.88 on the validation data set for extracting 13 SDoH factors, surpassing the benchmark by 5%. It excelled in detailed subtype extraction, while the benchmark performed better in identifying rarely annotated SDoH information in BC Cancer data set. Overall, 60,717 SDoH factors and associated details were extracted from BC Cancer EHR oncology documents. The most frequently extracted SDoH factors included tobacco use, employment status, marital status, alcohol consumption, and living status, occurring between 8k to 12k times.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSION<\/jats:title>\n                    <jats:p>This study demonstrates the potential of an NLP pipeline to extract SDoH factors from clinical notes, with strong performance on limited data, although data set\u2013specific adjustments are needed for broader application across institutions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1200\/cci-24-00317","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T20:00:10Z","timestamp":1753300810000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":5,"title":["Extraction of Social Determinants of Health From Electronic Health Records Using Natural Language Processing"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7034-6430","authenticated-orcid":true,"given":"Zhenghua","family":"Chen","sequence":"first","affiliation":[{"name":"BC Cancer Kelowna, Kelowna, Canada"},{"name":"Computer Science, University of British Columbia\u2014Okanagan, Kelowna, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7080-2437","authenticated-orcid":true,"given":"Patricia","family":"Lasserre","sequence":"additional","affiliation":[{"name":"Computer Science, University of British Columbia\u2014Okanagan, Kelowna, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0611-539X","authenticated-orcid":true,"given":"Angela","family":"Lin","sequence":"additional","affiliation":[{"name":"BC Cancer Kelowna, Kelowna, Canada"},{"name":"Department of Surgery, University of British Columbia, Vancouver, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2149-7274","authenticated-orcid":true,"given":"Rasika","family":"Rajapakshe","sequence":"additional","affiliation":[{"name":"BC Cancer Kelowna, Kelowna, Canada"},{"name":"Computer Science, University of British Columbia\u2014Okanagan, Kelowna, Canada"},{"name":"Department of Surgery, University of British Columbia, Vancouver, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"233","reference":[{"key":"e_1_3_6_2_2","volume-title":"NCHHSTP Social Determinants of Health","author":"Centers for Disease Control and Prevention","unstructured":"Centers for Disease Control and Prevention: NCHHSTP Social Determinants of Health. https:\/\/www.cdc.gov\/nchhstp\/socialdeterminants\/faq.html"},{"key":"e_1_3_6_3_2","volume-title":"Social Determinants of Health","author":"World Health Organization","unstructured":"World Health Organization: Social Determinants of Health. https:\/\/www.who.int\/health-topics\/social-determinants-of-health"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amepre.2008.05.006"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.soncn.2017.11.001"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.EPI-07-0633"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-019-05340-7"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12875-017-0599-z"},{"key":"e_1_3_6_9_2","volume-title":"Disaggregated Demographic Data Collection in British Columbia: The Grandmother Perspective","author":"BC Office of the Human Rights Commissioner","year":"2020","unstructured":"BC Office of the Human Rights Commissioner: Disaggregated Demographic Data Collection in British Columbia: The Grandmother Perspective. 2020. https:\/\/bchumanrights.ca\/wp-content\/uploads\/BCOHRC_Sept2020_Disaggregated-Data-Report_FINAL.pdf"},{"key":"e_1_3_6_10_2","volume-title":"Pan-Canadian Data Strategy","author":"Canadian Partnership Against Cancer; 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Oshawa, Ontario Tech University Faculty of Health Sciences and Toronto, York University School of Health Policy and Management. 2020. https:\/\/thecanadianfacts.org\/The_Canadian_Facts-2nd_ed.pdf"},{"key":"e_1_3_6_14_2","volume-title":"Social Determinants of Health Dataset Repository","author":"Zhenghua-404","unstructured":"Zhenghua-404: Social Determinants of Health Dataset Repository. https:\/\/github.com\/Zhenghua-404\/sdoh"},{"key":"e_1_3_6_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocad073"},{"key":"e_1_3_6_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpa.2021.100058"},{"key":"e_1_3_6_17_2","volume-title":"John Snow Labs Home","author":"John Snow Labs","unstructured":"John Snow Labs: John Snow Labs Home. https:\/\/www.johnsnowlabs.com\/"},{"key":"e_1_3_6_18_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocad012"},{"key":"e_1_3_6_19_2","unstructured":"Reference deleted"},{"key":"e_1_3_6_20_2","unstructured":"Reference deleted"},{"key":"e_1_3_6_21_2","unstructured":"Reference deleted"},{"key":"e_1_3_6_22_2","unstructured":"Reference deleted"},{"key":"e_1_3_6_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amepre.2017.05.011"},{"key":"e_1_3_6_24_2","doi-asserted-by":"publisher","DOI":"10.1503\/cmaj.160177"}],"container-title":["JCO Clinical Cancer Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ascopubs.org\/doi\/pdfdirect\/10.1200\/CCI-24-00317","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T20:00:13Z","timestamp":1753300813000},"score":1,"resource":{"primary":{"URL":"https:\/\/ascopubs.org\/doi\/10.1200\/CCI-24-00317"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":23,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["10.1200\/CCI-24-00317"],"URL":"https:\/\/doi.org\/10.1200\/cci-24-00317","relation":{},"ISSN":["2473-4276"],"issn-type":[{"value":"2473-4276","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"2024-12-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-02","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-06-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e2400317"}}