Discovering Invariances in Healthcare Neural Networks

TL;DR

This paper introduces a method to empirically identify invariances in healthcare neural networks by learning input transformations that do not alter predictions, revealing which features models rely on or ignore.

Contribution

The paper presents a novel technique combining Wasserstein distance minimization and regularization to discover invariances in pre-trained healthcare models, with theoretical analysis and practical applications.

Findings

Identified variables ignored by LSTM models in clinical time series.

Discovered words BioBERT is invariant to in clinical notes.

Analyzed invariances in models trained for adversarial robustness.

Abstract

We study the invariance characteristics of pre-trained predictive models by empirically learning transformations on the input that leave the prediction function approximately unchanged. To learn invariant transformations, we minimize the Wasserstein distance between the predictive distribution conditioned on the data instances and the predictive distribution conditioned on the transformed data instances. To avoid finding degenerate or perturbative transformations, we add a similarity regularization to discourage similarity between the data and its transformed values. We theoretically analyze the correctness of the algorithm and the structure of the solutions. Applying the proposed technique to clinical time series data, we discover variables that commonly-used LSTM models do not rely on for their prediction, especially when the LSTM is trained to be adversarially robust. We also analyze the invariances of BioBERT on clinical notes and discover words that it is invariant to.