# A natural language processing approach to identify social risk factors from neonatal intensive care unit patients’ (NICU) electronic health records

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $30,209

## Abstract

Adverse social drivers/determinants of health (SDOH) such as food, housing, and income insecurity (hereby
referred to as social risk factors) can have significant impact on an infant’s health and may exacerbate health
inequities. To reduce health burdens and improve clinical decision-making, it is necessary to understand how
social risk factors are associated with hospital readmissions among infants in the neonatal intensive care unit
(NICU). However, measuring social risk factors is challenging, as SDOH are not well documented in the
electronic health record (EHR), and current EHR platforms lack screening tools to identify social risk factors in
NICU clinical settings. Patient clinical notes contain a wealth of SDOH information, but researchers face the
challenge of manually reviewing and extracting this information, which is time-consuming. The objective of
this proposal is to: 1) leverage natural language processing to identify and extract social risk factors
from unstructured clinical note EHR data of NICU patients and 2) understand how unmet social risk
factors predict hospital readmissions among infants discharged from the NICU using both structured
and unstructured EHR data. To achieve this objective, this study will fine-tune developed Bidirectional
Encoder Representations from Transformers (BERT) models on NICU clinical notes to improve their ability to
recognize and extract social risk factors (Aim 1). Investigators will be able to better identify and classify infants
with food, housing, and income insecurity, and understand how social risk factors are characterized in NICU
clinical notes. The performance, validation, and generalizability of these models will be evaluated with standard
metrics. Additionally, this study will assess how social risk factors are associated with 30-day hospital
readmission among infants discharged from the NICU (Aim 2). Our central hypothesis is we will be able to
uncover additional information related to a patient’s experience with social risk factors, and these risk factors
will be associated with higher rates of 30-day hospital readmission. We will explore how associational effect
estimates change using solely structured data (diagnosis codes) compared to using both structured and
unstructured data. This project will advance steps to streamline and automate the extraction of valuable SDOH
data from clinical notes and guide clinical decision-making in NICU care. The long-term goal of this project is
to harness the power of advanced data science methods to optimize patientcare and management for unmet
social needs and to improve health equity thus reducing health utilization outcomes. This research aligns well
with NIMHD’s Division of Clinical and Health Service Research. The proposed training, guided by an expert
mentorship team, will enrich the applicant’s knowledge of and skills in clinical informatics for health disparities,
data science for social epidemiology, and natural language processing for class...

## Key facts

- **NIH application ID:** 10997007
- **Project number:** 1F31MD019214-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Shivani Mehta
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $30,209
- **Award type:** 1
- **Project period:** 2024-11-01 → 2025-06-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10997007

## Citation

> US National Institutes of Health, RePORTER application 10997007, A natural language processing approach to identify social risk factors from neonatal intensive care unit patients’ (NICU) electronic health records (1F31MD019214-01A1). Retrieved via MORVS 2026-10-25 from https://api.morvs.ai/grant/nih/10997007. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*