Social and behavioral determinants of health (SBDH) play a crucial role in
health outcomes and are frequently documented in clinical text. Automatically
extracting SBDH information from clinical text relies on publicly available
good-quality datasets. However, existing SBDH datasets exhibit substantial
limitations in their availability and coverage. In this study, we introduce
Synth-SBDH, a novel synthetic dataset with detailed SBDH annotations,
encompassing status, temporal information, and rationale across 15 SBDH
categories. We showcase the utility of Synth-SBDH on three tasks using
real-world clinical datasets from two distinct hospital settings, highlighting
its versatility, generalizability, and distillation capabilities. Models
trained on Synth-SBDH consistently outperform counterparts with no Synth-SBDH
training, achieving up to 62.5% macro-F improvements. Additionally, Synth-SBDH
proves effective for rare SBDH categories and under-resource constraints. Human
evaluation demonstrates a Human-LLM alignment of 71.06% and uncovers areas for
future refinements.

社会和行为健康决定因素在健康结果中起着关键作用，本研究引入了具有详细 SBDH 注释的新型合成数据集 Synth-SBDH，并展示了其在临床数据集上的实用性、泛化能力和提炼能力。

Synth-SBDH：临床文本中的社会行为和健康决定因素的合成数据集

Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of  Health for Clinical Text

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.

该研究介绍了一个新的数据集 SDOH-NLI，基于公开可用的笔记，通过将社会和行为决定因素提取为自然语言推理任务，从而帮助健康护理提供者系统地识别提供适当护理和解决差异的机会。