Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health

TL;DR

This paper introduces a micromodel architecture for mental health applications that enhances interpretability, reusability, and performance in low-resource settings, addressing key limitations of traditional statistical models.

Contribution

The paper presents a novel micromodel approach that embeds domain knowledge and offers explanations, improving interpretability and adaptability in mental health tasks.

Findings

Consistently strong results across multiple mental health tasks.

Enhanced interpretability over alternative methods.

Effective in low-resource scenarios.

Abstract

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.