Despite the increasing demand for AI-based mental health monitoring tools,
their practical utility for clinicians is limited by the lack of
interpretability.The CLPsych 2024 Shared Task (Chim et al., 2024) aims to
enhance the interpretability of Large Language Models (LLMs), particularly in
mental health analysis, by providing evidence of suicidality through linguistic
content. We propose a dual-prompting approach: (i) Knowledge-aware evidence
extraction by leveraging the expert identity and a suicide dictionary with a
mental health-specific LLM; and (ii) Evidence summarization by employing an
LLM-based consistency evaluator. Comprehensive experiments demonstrate the
effectiveness of combining domain-specific information, revealing performance
improvements and the approach's potential to aid clinicians in assessing mental
state progression.

提出了一种双提示方法，通过使用领域特定的信息和基于语言模型的一致性评估器，实现知识感知证据提取和证据总结，并综合实验证明了该方法的有效性和帮助临床医生评估心理状态进展的潜力。

一种用于可解释的心理健康语言模型的双提示方法

A Dual-Prompting for Interpretable Mental Health Language Models

Language models have the potential to assess mental health using social media
data. By analyzing online posts and conversations, these models can detect
patterns indicating mental health conditions like depression, anxiety, or
suicidal thoughts. They examine keywords, language markers, and sentiment to
gain insights into an individual's mental well-being. This information is
crucial for early detection, intervention, and support, improving mental health
care and prevention strategies. However, using language models for mental
health assessments from social media has two limitations: (1) They do not
compare posts against clinicians' diagnostic processes, and (2) It's
challenging to explain language model outputs using concepts that the clinician
can understand, i.e., clinician-friendly explanations. In this study, we
introduce Process Knowledge-infused Learning (PK-iL), a new learning paradigm
that layers clinical process knowledge structures on language model outputs,
enabling clinician-friendly explanations of the underlying language model
predictions. We rigorously test our methods on existing benchmark datasets,
augmented with such clinical process knowledge, and release a new dataset for
assessing suicidality. PK-iL performs competitively, achieving a 70% agreement
with users, while other XAI methods only achieve 47% agreement (average
inter-rater agreement of 0.72). Our evaluations demonstrate that PK-iL
effectively explains model predictions to clinicians.

通过对社交媒体数据进行分析，研究人员转向使用语言模型来评估精神健康，并引入了一种新的学习范式（PK-iL）来提供临床友好的解释，从而提高精神健康护理和预防策略的效果。

基于过程知识的学习，为临床医生提供友好的解释

Process Knowledge-infused Learning for Clinician-friendly Explanations

Identifying suicidality including suicidal ideation, attempts, and risk
factors in electronic health record data in clinical notes is difficult. A
major difficulty is the lack of training samples given the small number of true
positive instances among the increasingly large number of patients being
screened. This paper describes a novel methodology that identifies suicidality
in clinical notes by addressing this data sparsity issue through zero-shot
learning. U.S. Veterans Affairs clinical notes served as data. The training
dataset label was determined using diagnostic codes of suicide attempt and
self-harm. A base string associated with the target label of suicidality was
used to provide auxiliary information by narrowing the positive training cases
to those containing the base string. A deep neural network was trained by
mapping the training documents contents to a semantic space. For comparison, we
trained another deep neural network using the identical training dataset labels
and bag-of-words features. The zero shot learning model outperformed the
baseline model in terms of AUC, sensitivity, specificity, and positive
predictive value at multiple probability thresholds. In applying a 0.90
probability threshold, the methodology identified notes not associated with a
relevant ICD 10 CM code that documented suicidality, with 94 percent accuracy.
This new method can effectively identify suicidality without requiring manual
annotation.

本文介绍了一种通过零样本学习识别临床笔记中自杀、自残等自杀风险要素的新方法。通过在基于目标标签的基本字符串的帮助下，使用诊断编码确定培训数据集标签，使用神经网络映射训练文档内容到语义空间，相比较于传统的深度神经网络模型，在多个概率阈值上提高了 AUC、灵敏度、特异度和正预测值等评价指标。在应用 0.90 概率阈值时，该方法可以在不需要手动标注的情况下，以 94% 的准确度识别与自杀相关的临床笔记。