Test-Time Training with Diversified Local Aggregation Consistency for Mortality Prediction using Clinical Time Series

Mortality prediction is necessary for patients in the Intensive Care Unit (ICU). Clinical time series provide essential insights for making accurate predictions. However, existing prediction models often struggle with domain shifts when applied across different domains. Privacy concern hinders model calibration due to the forbidden data sharing across domains. Test-Time Training (TTT) has been increasingly researched to tackle the above issues by updating a source model to each single target sample before inference. While massive vision-based TTT methods are proposed, deploying TTT in clinical time series still faces the unique challenge of temporal imbalance: Time points tend to cluster around specific periods in some patients. Neglecting the temporal imbalance in TTT can make the model biased toward dense local pattern, resulting in unsatisfactory prediction. To overcome this challenge, we propose a novel Test-Time Training method with Diversified Local Aggregation Consistency (DLAC-TTT). During the test-time update, DLAC-TTT focuses on the distinct temporal distributions within each patient, enforcing their local diversity and global consistency through aggregation. In this way, it can mitigate the over-reliance on specific local patterns and well integrate diverse local patterns for global learning. Extensive experiments show that DLAC-TTT can boost the generalization performance across real-world clinical datasets from different medical institutes.