  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.
