Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health
This addresses the need for interpretable and adaptable AI systems in mental health, where data is scarce and decisions have high impact, though it appears incremental in its application of existing concepts to a specific domain.
The paper tackles the problem of statistical models lacking explainability, reusability, and efficiency in low-resource settings, particularly in mental health, by introducing a micromodel architecture that achieves strong results on tasks like depression and PTSD classification.
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.