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

In many applications, the labeled data at the learner's disposal is subject
to privacy constraints and is relatively limited. To derive a more accurate
predictor for the target domain, it is often beneficial to leverage publicly
available labeled data from an alternative domain, somewhat close to the target
domain. This is the modern problem of supervised domain adaptation from a
public source to a private target domain. We present two $(\epsilon,
\delta)$-differentially private adaptation algorithms for supervised
adaptation, for which we make use of a general optimization problem, recently
shown to benefit from favorable theoretical learning guarantees. Our first
algorithm is designed for regression with linear predictors and shown to solve
a convex optimization problem. Our second algorithm is a more general solution
for loss functions that may be non-convex but Lipschitz and smooth. While our
main objective is a theoretical analysis, we also report the results of several
experiments first demonstrating that the non-private versions of our algorithms
outperform adaptation baselines and next showing that, for larger values of the
target sample size or $\epsilon$, the performance of our private algorithms
remains close to that of the non-private formulation.

通过使用一种新的优化问题来训练，本文提出了两种隐私保护的监督适应算法，分别适用于具有线性预测器的回归和非凸但利普希兹平滑的损失函数，并且在实验中表明这两种算法的效果表现优于基线算法。