Stabilizing Linear Prediction Models using Autoencoder

TL;DR

This paper introduces a method to improve the stability of linear prediction models in high-dimensional healthcare data by using autoencoders to regularize feature correlations, enhancing clinical reliability.

Contribution

It proposes a novel stabilization scheme leveraging autoencoders to model and regularize higher order feature correlations, improving model stability in high-dimensional settings.

Findings

Significant improvement in feature stability over baselines.

Enhanced model estimation stability demonstrated.

Effective use of external unlabelled data for autoencoder training.

Abstract

To date, the instability of prognostic predictors in a sparse high dimensional model, which hinders their clinical adoption, has received little attention. Stable prediction is often overlooked in favour of performance. Yet, stability prevails as key when adopting models in critical areas as healthcare. Our study proposes a stabilization scheme by detecting higher order feature correlations. Using a linear model as basis for prediction, we achieve feature stability by regularising latent correlation in features. Latent higher order correlation among features is modelled using an autoencoder network. Stability is enhanced by combining a recent technique that uses a feature graph, and augmenting external unlabelled data for training the autoencoder network. Our experiments are conducted on a heart failure cohort from an Australian hospital. Stability was measured using Consistency index for feature subsets and signal-to-noise ratio for model parameters. Our methods demonstrated significant improvement in feature stability and model estimation stability when compared to baselines.