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

Andrew Lee, Jonathan K. Kummerfeld, Larry An, Rada Mihalcea

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 in-
tegrate domain expertise. These factors limit
their use, particularly in settings such as men-
tal health, where it is difficult to annotate
datasets and model outputs have significant im-
pact. We introduce a micromodel architecture
to address these challenges. Our approach al-
lows researchers to build interpretable repre-
sentations 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 suici-
dal risk assessment. Our systems consistently
produce strong results, even in low-resource
scenarios, and are more interpretable than al-
ternative methods.