SynthEHR-Eviction: Enhancing Eviction SDoH Detection with LLM-Augmented Synthetic EHR Data
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Eviction is a significant yet understudied social determinants of health
(SDoH), linked to housing instability, unemployment, and mental health. While
eviction appears in unstructured electronic health records (EHRs), it is rarely
coded in structured fields, limiting downstream applications. We introduce
SynthEHR-Eviction, a scalable pipeline combining LLMs, human-in-the-loop
annotation, and automated prompt optimization (APO) to extract eviction
statuses from clinical notes. Using this pipeline, we created the largest
public eviction-related SDoH dataset to date, comprising 14 fine-grained
categories. Fine-tuned LLMs (e.g., Qwen2.5, LLaMA3) trained on
SynthEHR-Eviction achieved Macro-F1 scores of 88.8% (eviction) and 90.3% (other
SDoH) on human validated data, outperforming GPT-4o-APO (87.8%, 87.3%),
GPT-4o-mini-APO (69.1%, 78.1%), and BioBERT (60.7%, 68.3%), while enabling
cost-effective deployment across various model sizes. The pipeline reduces
annotation effort by over 80%, accelerates dataset creation, enables scalable
eviction detection, and generalizes to other information extraction tasks.
