7 research outputs found
SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes
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.Comment: Findings of EMNLP 202
Instability in clinical risk stratification models using deep learning
While it has been well known in the ML community that deep learning models
suffer from instability, the consequences for healthcare deployments are under
characterised. We study the stability of different model architectures trained
on electronic health records, using a set of outpatient prediction tasks as a
case study. We show that repeated training runs of the same deep learning model
on the same training data can result in significantly different outcomes at a
patient level even though global performance metrics remain stable. We propose
two stability metrics for measuring the effect of randomness of model training,
as well as mitigation strategies for improving model stability.Comment: Accepted for publication in Machine Learning for Health (ML4H) 202
All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass
Multi-Task Learning (MTL) models have shown their robustness, effectiveness,
and efficiency for transferring learned knowledge across tasks. In real
industrial applications such as web content classification, multiple
classification tasks are predicted from the same input text such as a web
article. However, at the serving time, the existing multitask transformer
models such as prompt or adaptor based approaches need to conduct N forward
passes for N tasks with O(N) computation cost. To tackle this problem, we
propose a scalable method that can achieve stronger performance with close to
O(1) computation cost via only one forward pass. To illustrate real application
usage, we release a multitask dataset on news topic and style classification.
Our experiments show that our proposed method outperforms strong baselines on
both the GLUE benchmark and our news dataset. Our code and dataset are publicly
available at https://bit.ly/mtop-code
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