Global Interpretability via Automated Preprocessing: A Framework Inspired by Psychiatric Questionnaires

Researcher Eric V. Strobl has introduced a groundbreaking machine learning framework called REFINE (Redundancy-Exploiting Follow-up-Informed Nonlinear Enhancement) that addresses the critical tension between predictive accuracy and interpretability in sensitive domains like psychiatry. The paper, published on arXiv, presents a novel two-stage approach inspired by how medical researchers handle noisy questionnaire data: by decoupling complex preprocessing from simple predictive modeling. This architecture restricts nonlinear transformations to a preprocessing module that stabilizes input features, then applies a fully interpretable linear model to make predictions. The method directly tackles the challenge where flexible models like deep neural networks achieve better accuracy but erode clinical trust due to their black-box nature.

REFINE's technical innovation lies in concentrating all nonlinear capacity into the preprocessing stage while keeping the prognostic relationship transparently linear. This allows for global interpretability through a coefficient matrix rather than relying on post-hoc local attribution methods like SHAP or LIME. In experiments across psychiatric and non-psychiatric longitudinal prediction tasks, REFINE outperformed other interpretable approaches while maintaining clear attribution of prognostic factors. The framework represents a significant step toward trustworthy AI in healthcare, where understanding why a model makes certain predictions is as important as the predictions themselves. As AI adoption in clinical settings accelerates, methods like REFINE could become standard for balancing performance with the transparency needed for medical decision-making.