  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.
