Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise. These factors limit their use, particularly in settings such as mental health, where it is difficult to annotate datasets and model outputs have significant impact. We introduce a micromodel architecture to address these challenges. Our approach allows researchers to build interpretable representations that embed domain knowledge and provide explanations throughout the model's decision process. We demonstrate the idea on multiple mental health tasks: depression classification, PTSD classification, and suicidal risk assessment. Our systems consistently produce strong results, even in low-resource scenarios, and are more interpretable than alternative methods.

介绍了一种微模型架构，解决了在精神卫生等难以注释数据集和模型输出具有重大影响的场景下，统计模型准确性高但难以解释、低资源场景下性能下降、不可重复使用、难以集成领域专业知识等问题，该方法具有可解释性，能够嵌入领域知识，并在模型决策过程中提供解释，在多个精神卫生任务中表现出强大的性能。

高效、可解释、可重用系统的微型模型：精神健康案例研究