MCA-based Rule Mining Enables Interpretable Inference in Clinical Psychiatry

TL;DR

This paper introduces a novel MCA-based rule mining method that enables fast, interpretable, and accurate clinical psychiatry models, facilitating discovery and diagnosis in high-dimensional patient data.

Contribution

The paper presents a new categorical rule mining technique based on MCA, capable of handling large feature sets efficiently and applied to transdiagnostic psychiatric disorder screening.

Findings

Method is at least 100 times faster than existing techniques.

Provides interpretability and comparable accuracy across benchmark datasets.

Effective in high-dimensional clinical data analysis.

Abstract

Development of interpretable machine learning models for clinical healthcare applications has the potential of changing the way we understand, treat, and ultimately cure, diseases and disorders in many areas of medicine. These models can serve not only as sources of predictions and estimates, but also as discovery tools for clinicians and researchers to reveal new knowledge from the data. High dimensionality of patient information (e.g., phenotype, genotype, and medical history), lack of objective measurements, and the heterogeneity in patient populations often create significant challenges in developing interpretable machine learning models for clinical psychiatry in practice. In this paper we take a step towards the development of such interpretable models. First, by developing a novel categorical rule mining method based on Multivariate Correspondence Analysis (MCA) capable of handling datasets with large numbers of features, and second, by applying this method to build transdiagnostic Bayesian Rule List models to screen for psychiatric disorders using the Consortium for Neuropsychiatric Phenomics dataset. We show that our method is not only at least 100 times faster than state-of-the-art rule mining techniques for datasets with 50 features, but also provides interpretability and comparable prediction accuracy across several benchmark datasets.