Enhancing Maternal Health Surveillance in the United States Through Natural Language Processing.
Maternal health outcomes are essential indicators of overall health care quality and societal well-being. However, in the United States, the maternal health surveillance is often inaccurate, restricting the clinical utility of the data gathered. The limits imposed by these inaccuracies restrict time...
| Publicado en: | American Journal of Perinatology Vol. 43; no. 10; pp. 1414 - 1419 |
|---|---|
| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Thieme Medical Publishing Inc.
Jul2026
|
| 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=195179470&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195179470 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07351631 GQK jtl: American Journal of Perinatology issn: 07351631 maglogo: N pubinfo: dt: Jul2026 vid: 43 iid: 10 pid: 2811 pub: Thieme Medical Publishing Inc. place: New York, New York artinfo: ui: 195179470 10.1055/a-2764-2341 195179470 ppf: 1414 ppct: 5 formats: tig: atl: Enhancing Maternal Health Surveillance in the United States Through Natural Language Processing. aug: au: Horgan, Rebecca Kawakita, Tetsuya Saade, George affil: Department of Obstetrics and Gynecology, Macon and Joan Brock Virginia Health Sciences, Old Dominion University, Norfolk, Virginia, United States sug: subj: Women's Health In Pregnancy Natural Language Processing Population Surveillance Electronic Health Records Machine Learning Pregnancy Female United States Data Analysis Linguistics Social Determinants of Health Pregnancy-Induced Hypertension Complications Resource Allocation Information Retrieval Sensitivity and Specificity Reproducibility of Results Random Forest Venous Thromboembolism Phenotype Pregnancy Complications Prevention and Control Predictive Validity Prediction Models Female ab: Maternal health outcomes are essential indicators of overall health care quality and societal well-being. However, in the United States, the maternal health surveillance is often inaccurate, restricting the clinical utility of the data gathered. The limits imposed by these inaccuracies restrict timely policy responses and hinder effective innovations, despite the increasing availability of electronic health records. This paper explores the potential use of natural language processing in improving maternal health surveillance. By combining rule-based linguistic processing with machine learning, natural language processing can transform narrative text into structured, analyzable data, allowing it to be used for predictive purposes, as well as the development of real-time public health surveillance systems. Key Points: Maternal health surveillance is often inaccurate, restricting the clinical utility of the data. Natural language processing can extract key insights from unstructured clinical notes. Artificial intelligence-driven surveillance in obstetrics may improve data accuracy and timeliness. Ethical use of natural language processing needs to ensure privacy, bias control, and validation. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|