Work

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

Year: 2025

Type: article

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.

Source: Scientific Reports

DOI: 10.1038/s41598-025-13542-x

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

Institutions: Johns Hopkins University, Johns Hopkins Medicine, University of Florida

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,690