Evidence map›Paper›PMID 40783439›Full record

ArticleScientific reports2025

Unveiling social determinants of health impact on adverse pregnancy outcomes through natural language processing.

Nidhi Soley, MaKhaila Bentil, Jash Shah, Masoud Rouhizadeh, Casey Overby Taylor

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nidhi SoleyDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA. nsoley1@jhu.edu.
MaKhaila BentilDepartment of Computer Science Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
Jash ShahDepartment of Computer Science Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
Masoud Rouhizadeh *Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA.
Casey Overby Taylor *Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the role of Social Determinants of Health (SDoH) in pregnancy outcomes is critical for improving maternal and infant health yet extracting SDoH from unstructured electronic health records remains challenging. We trained and evaluated natural language processing (NLP) models for SDoH extraction from clinical notes in the MIMIC-III database (86 notes), and externally evaluated them on the MIMIC-IV database (171 notes) to assess generalizability. Focusing on social support, occupation, and substance use, we compared rule-based, word embedding, and contextual language models. The ClinicalBERT model with decision tree classifier achieved the highest performance for social support (F1: 0.92), while keyword processing excelled for occupation (F1: 0.74), and word embeddings with random forest performed best for substance use (F1: 0.83). Logistic regression revealed significant associations between pregnancy complications and both substance use (OR 6.47, p < 0.001) and social support (OR 0.07, p < 0.001). Our study demonstrates the feasibility of NLP for SDoH extraction and underscores their clinical relevance in maternal health.

Indexed as

Natural Language ProcessingPregnancy ComplicationsPregnancy OutcomeSocial Determinants of HealthAdultDatabases, FactualElectronic Health RecordsFemaleHumansPregnancySocial SupportMaternal healthNatural language processingOccupationSocial determinants of healthSocial supportSubstance use

Identifiers

PMID40783439
PMCPMC12335529

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.