SDOH-NLI: a Dataset for Inferring Social Determinants of Health from
  Clinical Notes
Adam D. Lelkes Eric Loreaux Tal Schuster Ming-Jun Chen Alvin Rajkomar
Social and behavioral determinants of health (SDOH) play a significant role
in shaping health outcomes, and extracting these determinants from clinical
notes is a first step to help healthcare providers systematically identify
opportunities to provide appropriate care and address disparities. Progress on
using NLP methods for this task has been hindered by the lack of high-quality
publicly available labeled data, largely due to the privacy and regulatory
constraints on the use of real patients' information. This paper introduces a
new dataset, SDOH-NLI, that is based on publicly available notes and which we
release publicly. We formulate SDOH extraction as a natural language inference
(NLI) task, and provide binary textual entailment labels obtained from human
raters for a cross product of a set of social history snippets as premises and
SDOH factors as hypotheses. Our dataset differs from standard NLI benchmarks in
that our premises and hypotheses are obtained independently. We evaluate both
"off-the-shelf" entailment models as well as models fine-tuned on our data, and
highlight the ways in which our dataset appears more challenging than commonly
used NLI datasets.