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In this pilot study, we conducted a preliminary evaluation of natural language processing (NLP) in extracting signs and symptoms (SS) of HDP from clinical notes within electronic health records (EHRs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective observational pilot study used EHR data from patients admitted for labor and birth (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u200983,003 clinical notes from 17,775 patients). Four SS categories were extracted: elevated blood pressure, neurological, renal, and hepatic\/hematologic. Five machine learning models and ClinicalBERT were trained and tested using five-fold cross-validation. The best-performing model was applied to the full dataset. Bivariate analyses were performed to examine (1) differences in HDP diagnoses based on ICD-10 codes (gestational hypertension, preeclampsia, and eclampsia) by SS documentation and (2) differences in SS documentation by patient race and ethnicity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      XGBoost demonstrated the highest macro-average F1-score (0.75). Elevated blood pressure showed the highest F1-score (0.87), followed by neurological SS (0.77). In the full dataset, 24.3% of clinical notes and 42.3% of patients had documentation of at least one SS category. A higher proportion of HDP diagnoses was observed with an increased number of SS categories documented (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; .001). A higher proportion of non-Hispanic Black patients had documentation of SS across all categories.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>NLP can extract SS with moderate accuracy, supporting feasibility for larger-scale extraction. Findings also highlight differences in SS documentation by patient race and ethnicity. Future research is needed to improve NLP performance, including expanding annotated data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10995-026-04275-y","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T07:47:28Z","timestamp":1779263248000},"page":"809-819","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Extracting Signs and Symptoms of Hypertensive Disorders in Pregnancy from Clinical Notes Using Natural Language Processing"],"prefix":"10.1007","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0864-7867","authenticated-orcid":false,"given":"Jihye Kim","family":"Scroggins","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ismael I.","family":"Hulchafo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxim","family":"Topaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronica","family":"Barcelona","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"issue":"6","key":"4275_CR1","doi-asserted-by":"publisher","first-page":"e237","DOI":"10.1097\/AOG.0000000000003891","volume":"135","author":"ACOG","year":"2020","unstructured":"ACOG. 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