DeepCoDA: personalized interpretability for compositional health data

TL;DR

DeepCoDA introduces a novel framework for personalized interpretability in high-dimensional compositional health data, ensuring reliable, patient-specific insights without sacrificing model accuracy.

Contribution

It extends precision health modeling to compositional data and provides personalized, coherent interpretability through a new deep learning architecture.

Findings

Maintains state-of-the-art performance across 25 datasets.

Provides patient-specific feature attributions.

Ensures interpretability is coherent for compositional data.

Abstract

Interpretability allows the domain-expert to directly evaluate the model's relevance and reliability, a practice that offers assurance and builds trust. In the healthcare setting, interpretable models should implicate relevant biological mechanisms independent of technical factors like data pre-processing. We define personalized interpretability as a measure of sample-specific feature attribution, and view it as a minimum requirement for a precision health model to justify its conclusions. Some health data, especially those generated by high-throughput sequencing experiments, have nuances that compromise precision health models and their interpretation. These data are compositional, meaning that each feature is conditionally dependent on all other features. We propose the Deep Compositional Data Analysis (DeepCoDA) framework to extend precision health modelling to high-dimensional compositional data, and to provide personalized interpretability through patient-specific weights. Our architecture maintains state-of-the-art performance across 25 real-world data sets, all while producing interpretations that are both personalized and fully coherent for compositional data.