Clusters in Explanation Space: Inferring disease subtypes from model explanations

TL;DR

This paper presents a novel method that uses explanations from a diagnostic classifier to identify disease subtypes, outperforming traditional clustering on raw data in high-dimensional biomedical datasets.

Contribution

The paper introduces a new approach that leverages explanation space from classifiers to discover disease subtypes more effectively than classical methods.

Findings

Cluster analysis on explanation space outperforms classical clustering on original data.

Method successfully identifies known disease subtypes in brain imaging and transcriptome datasets.

Approach is applicable to various subtype discovery tasks beyond biomedical data.

Abstract

Identification of disease subtypes and corresponding biomarkers can substantially improve clinical diagnosis and treatment selection. Discovering these subtypes in noisy, high dimensional biomedical data is often impossible for humans and challenging for machines. We introduce a new approach to facilitate the discovery of disease subtypes: Instead of analyzing the original data, we train a diagnostic classifier (healthy vs. diseased) and extract instance-wise explanations for the classifier's decisions. The distribution of instances in the explanation space of our diagnostic classifier amplifies the different reasons for belonging to the same class - resulting in a representation that is uniquely useful for discovering latent subtypes. We compare our ability to recover subtypes via cluster analysis on model explanations to classical cluster analysis on the original data. In multiple datasets with known ground-truth subclasses, most compellingly on UK Biobank brain imaging data and transcriptome data from the Cancer Genome Atlas, we show that cluster analysis on model explanations substantially outperforms the classical approach. While we believe clustering in explanation space to be particularly valuable for inferring disease subtypes, the method is more general and applicable to any kind of sub-type identification.