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Medical events of interest, such as mortality, often happen at a low rate inelectronic medical records, as most admitted patients survive. Training modelswith this imbalance rate (class density discrepancy) may lead to suboptimalprediction. Traditionally this problem is addressed through ad-hoc methods suchas resampling or reweighting but performance in many cases is still limited. Wepropose a framework for training models for this imbalance issue: 1) we firstdecouple the feature extraction and classification process, adjusting trainingbatches separately for each component to mitigate bias caused by class densitydiscrepancy; 2) we train the network with both a density-aware loss and alearnable cost matrix for misclassifications. We demonstrate our model'simproved performance in real-world medical datasets (TOPCAT and MIMIC-III) toshow improved AUC-ROC, AUC-PRC, Brier Skill Score compared with the baselinesin the domain.

Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data