  Social determinants of health (SDOH) affect health outcomes, and knowledge of
SDOH can inform clinical decision-making. Automatically extracting SDOH
information from clinical text requires data-driven information extraction
models trained on annotated corpora that are heterogeneous and frequently
include critical SDOH. This work presents a new corpus with SDOH annotations, a
novel active learning framework, and the first extraction results on the new
corpus. The Social History Annotation Corpus (SHAC) includes 4,480 social
history sections with detailed annotation for 12 SDOH characterizing the
status, extent, and temporal information of 18K distinct events. We introduce a
novel active learning framework that selects samples for annotation using a
surrogate text classification task as a proxy for a more complex event
extraction task. The active learning framework successfully increases the
frequency of health risk factors and improves automatic extraction of these
events over undirected annotation. An event extraction model trained on SHAC
achieves high extraction performance for substance use status (0.82-0.93 F1),
employment status (0.81-0.86 F1), and living status type (0.81-0.93 F1) on data
from three institutions.
