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。

SDOH-NLI：从医疗记录中推断卫生社会因素的数据集

SDOH-NLI: a Dataset for Inferring Social Determinants of Health from  Clinical Notes

There have been many successful applications of sentence embedding methods.
However, it has not been well understood what properties are captured in the
resulting sentence embeddings depending on the supervision signals. In this
paper, we focus on two types of sentence embedding methods with similar
architectures and tasks: one fine-tunes pre-trained language models on the
natural language inference task, and the other fine-tunes pre-trained language
models on word prediction task from its definition sentence, and investigate
their properties. Specifically, we compare their performances on semantic
textual similarity (STS) tasks using STS datasets partitioned from two
perspectives: 1) sentence source and 2) superficial similarity of the sentence
pairs, and compare their performances on the downstream and probing tasks.
Furthermore, we attempt to combine the two methods and demonstrate that
combining the two methods yields substantially better performance than the
respective methods on unsupervised STS tasks and downstream tasks.

本文探讨了两种类型的句子嵌入方法对于语义文本相似度任务性能的影响，发现 fine-tunes 预训练语言模型在自然语言推断和单词预测任务中使用能够提高性能，而且将两种方法相结合能够比单个方法取得更好的效果。