Global Interpretability via Automated Preprocessing: A Framework Inspired by Psychiatric Questionnaires
arXiv:2602.23459v1 Announce Type: new Abstract: Psychiatric questionnaires are highly context sensitive and often only weakly predict subsequent symptom severity, which makes the prognostic relationship difficult to learn. Although flexible nonlinear models can improve predictive accuracy, their limited interpretability can erode clinical trust. In fields such as imaging and omics, investigators commonly address visit- and instrument-specific artifacts by extracting stable signal through preprocessing and then fitting an interpretable linear model. We adopt the same strategy for questionnaire data by decoupling preprocessing from prediction: we restrict nonlinear capacity to a baseline preprocessing module that estimates stable item values, and then learn a linear mapping from these stabilized baseline items to future severity. We refer to this two-stage method as REFINE (Redundancy-Exploiting Follow-up-Informed Nonlinear Enhancement), which concentrates nonlinearity in preprocessing
arXiv:2602.23459v1 Announce Type: new Abstract: Psychiatric questionnaires are highly context sensitive and often only weakly predict subsequent symptom severity, which makes the prognostic relationship difficult to learn. Although flexible nonlinear models can improve predictive accuracy, their limited interpretability can erode clinical trust. In fields such as imaging and omics, investigators commonly address visit- and instrument-specific artifacts by extracting stable signal through preprocessing and then fitting an interpretable linear model. We adopt the same strategy for questionnaire data by decoupling preprocessing from prediction: we restrict nonlinear capacity to a baseline preprocessing module that estimates stable item values, and then learn a linear mapping from these stabilized baseline items to future severity. We refer to this two-stage method as REFINE (Redundancy-Exploiting Follow-up-Informed Nonlinear Enhancement), which concentrates nonlinearity in preprocessing while keeping the prognostic relationship transparently linear and therefore globally interpretable through a coefficient matrix, rather than through post hoc local attributions. In experiments, REFINE outperforms other interpretable approaches while preserving clear global attribution of prognostic factors across psychiatric and non-psychiatric longitudinal prediction tasks.
Executive Summary
This article proposes a novel framework, REFINE, which decouples preprocessing from prediction to improve the interpretability of psychiatric questionnaires. By restricting nonlinearity to a baseline preprocessing module, REFINE learns a linear mapping from stabilized items to future severity, allowing for global interpretability through a coefficient matrix. The authors demonstrate REFINE's superiority over other interpretable approaches in psychiatric and non-psychiatric longitudinal prediction tasks. While REFINE addresses key concerns in psychiatric prediction, its applicability to other domains and the generalizability of its findings remain unclear. The method's ability to preserve clear global attribution of prognostic factors is a significant step forward in improving clinical trust and prediction accuracy.
Key Points
- ▸ The REFINE framework decouples preprocessing from prediction to enhance interpretability.
- ▸ Nonlinearity is restricted to a baseline preprocessing module, allowing for linear mapping and global interpretability.
- ▸ REFINE outperforms other interpretable approaches in psychiatric and non-psychiatric longitudinal prediction tasks.
Merits
Improved Interpretability
By restricting nonlinearity and learning a linear mapping, REFINE provides clear global attribution of prognostic factors, improving clinical trust and prediction accuracy.
Superior Performance
REFINE outperforms other interpretable approaches in both psychiatric and non-psychiatric longitudinal prediction tasks, demonstrating its versatility and effectiveness.
Demerits
Limited Applicability
The authors' focus on psychiatric questionnaires limits the framework's applicability to other domains, and its generalizability to diverse datasets is unclear.
Methodological Assumptions
The reliance on linear mapping and global interpretability may not be suitable for all datasets, and the method's performance may degrade with increasing complexity or noise.
Expert Commentary
The REFINE framework represents a significant step forward in improving the interpretability and accuracy of psychiatric questionnaires. By decoupling preprocessing from prediction, the authors effectively address key concerns in psychiatric prediction, such as limited generalizability and local attribution. While the method's applicability to other domains and datasets remains unclear, its potential to improve clinical trust and prediction accuracy in psychiatric prediction tasks is substantial. The article's findings also highlight the need for more research on interpretable machine learning models in high-stakes applications, such as healthcare and finance.
Recommendations
- ✓ Future research should focus on extending the REFINE framework to other domains and datasets to evaluate its generalizability and versatility.
- ✓ The development of more robust and flexible preprocessing modules is essential to accommodate diverse datasets and increasing complexity.