The lack of explainability using relevant clinical knowledge hinders the
adoption of Artificial Intelligence-powered analysis of unstructured clinical
dialogue. A wealth of relevant, untapped Mental Health (MH) data is available
in online communities, providing the opportunity to address the explainability
problem with substantial potential impact as a screening tool for both online
and offline applications. We develop a method to enhance attention in popular
transformer models and generate clinician-understandable explanations for
classification by incorporating external clinical knowledge. Inspired by how
clinicians rely on their expertise when interacting with patients, we leverage
relevant clinical knowledge to model patient inputs, providing meaningful
explanations for classification. This will save manual review time and engender
trust. We develop such a system in the context of MH using clinical practice
guidelines (CPG) for diagnosing depression, a mental health disorder of global
concern. We propose an application-specific language model called ProcesS
knowledge-infused cross ATtention (PSAT), which incorporates CPGs when
computing attention. Through rigorous evaluation on three expert-curated
datasets related to depression, we demonstrate application-relevant
explainability of PSAT. PSAT also surpasses the performance of nine baseline
models and can provide explanations where other baselines fall short. We
transform a CPG resource focused on depression, such as the Patient Health
Questionnaire (e.g. PHQ-9) and related questions, into a machine-readable
ontology using SNOMED-CT. With this resource, PSAT enhances the ability of
models like GPT-3.5 to generate application-relevant explanations.

通过加入临床知识，PSAT 模型提出了一种解释性的人工智能方法来分析和分类临床对话，为诊断抑郁症等心理健康问题提供了应用相关的解释。

利用临床实践指南的交叉注意力方法进行抑郁症诊断可解释性研究

A Cross Attention Approach to Diagnostic Explainability using Clinical  Practice Guidelines for Depression

Access to higher education is critical for minority populations and emergent
bilingual students. However, the language used by higher education institutions
to communicate with prospective students is often too complex; concretely, many
institutions in the US publish admissions application instructions far above
the average reading level of a typical high school graduate, often near the
13th or 14th grade level. This leads to an unnecessary barrier between students
and access to higher education. This work aims to tackle this challenge via
text simplification. We present PSAT (Professionally Simplified Admissions
Texts), a dataset with 112 admissions instructions randomly selected from
higher education institutions across the US. These texts are then
professionally simplified, and verified and accepted by subject-matter experts
who are full-time employees in admissions offices at various institutions.
Additionally, PSAT comes with manual alignments of 1,883 original-simplified
sentence pairs. The result is a first-of-its-kind corpus for the evaluation and
fine-tuning of text simplification systems in a high-stakes genre distinct from
existing simplification resources.

通过文本简化技术来解决高等教育机构与少数族裔学生及新兴双语学生间的交流障碍，研究创建了一种名为 PSAT 的文本数据集，包含 112 份从美国各高等教育机构随机选择的招生说明，并用被认可的高校招生专家进行了专业简化，并提供了共 1,883 个原始 - 简化句对的对齐，该数据集可用于评估和细化文本简化系统。