Layer-Wise Stability Optimization for Accurate and Reliable Prediction on Clinical Lab Data

Clinical lab tests are essential for disease diagnosis, medical treatment and predictive modeling tasks within healthcare. Traditional learning methods often struggle with lab test data that include imprecision ranges. In this paper, we tackle the challenge of improving prediction stability without generating additional training samples nor compromising accuracy. We reformulate the learning problem as a multi-objective optimization task, where accuracy and stability are both key objectives.To accomplish this, we develop a novel approach for calculating stability loss, by decomposing stability loss into a cumulative process, propagated layer by layer. We then formulate a stability-enhanced loss SELoss function to control the layer-wise output errors and maintain prediction precision. In addition, we design a multi-stage learning mechanism to control instability in each layer, especially in the initial layers. These components regulate the learning process, achieving a much improved balance between accuracy and stability. Using three real-world datasets, experimental results demonstrate that SELoss achieves more accurate and stable predictions across various tasks by reducing the instability of each layer. Also, as input perturbation increases, the rise in output instability slows down.