Extracting Signs and Symptoms of Hypertensive Disorders in Pregnancy from Clinical Notes Using Natural Language Processing.
Purpose: Hypertensive disorders in pregnancy (HDP) affect 16% of births in the United States. 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). Meth...
| Publicado en: | Maternal & Child Health Journal Vol. 30; no. 6; pp. 809 - 820 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194640730&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194640730 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10927875 N9J jtl: Maternal & Child Health Journal issn: 10927875 maglogo: N pubinfo: dt: Jun2026 vid: 30 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194640730 193882685 194640730 194640730 10.1007/s10995-026-04275-y 194640730 ppf: 809 ppct: 11 formats: tig: atl: Extracting Signs and Symptoms of Hypertensive Disorders in Pregnancy from Clinical Notes Using Natural Language Processing. aug: au: Scroggins, Jihye Kim Zhang, Zhihong Hulchafo, Ismael I. Topaz, Maxim Barcelona, Veronica affil: https://ror.org/0130frc33 School of Nursing, University of North Carolina at Chapel Hill, 120 Medical Dr, 27514, Chapel Hill, NC, USA sug: subj: Information Retrieval Signs and Symptoms Pregnancy-Induced Hypertension Diagnosis Electronic Health Records Natural Language Processing Utilization Human Female Adult Pregnancy United States Retrospective Design Record Review Nonexperimental Studies Pilot Studies Urban Areas Hospitals Pre-Eclampsia Diagnosis Pregnancy Complications, Cardiovascular Diagnosis Machine Learning Algorithms Support Vector Machine Random Forest Decision Trees Logistic Regression Sensitivity and Specificity Reproducibility of Results Health Status Disparities Bivariate Statistics Chi Square Test Data Analysis Software Descriptive Statistics Funding Source Adult: 19-44 years Female ab: Purpose: Hypertensive disorders in pregnancy (HDP) affect 16% of births in the United States. 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). Methods: This retrospective observational pilot study used EHR data from patients admitted for labor and birth (N = 83,003 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. Results: 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 (p <.001). A higher proportion of non-Hispanic Black patients had documentation of SS across all categories. Conclusion: 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. Significance: What is Already Known: Hypertensive disorders in pregnancy (HDP) affect approximately 16% of births in the United States. Previous studies have used natural language processing (NLP) to extract signs and symptoms of various health conditions, such as heart failure and cancer. However, it is underused in perinatal populations. What This Study Adds: This pilot study used NLP to extract signs and symptoms of HDP from narrative clinical notes with moderate performance, supporting methodological feasibility for larger-scale extraction. Findings also highlight differences in documentation patterns across racial and ethnic groups. Future research is needed to improve NLP performance, including expanding annotated data. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|