Domain Adaptation for Trauma Mortality Prediction in EHRs with Feature Disparity

Trauma mortality prediction from electronic health records (EHRs) with machine learning models has received growing attention in medical fields, but EHRs in different hospitals and sub-medical domain populations are often scarce due to expensive collection processes or privacy issues. Domain Adaptation (DA) has emerged as a promising approach in computer vision and natural language processing to improve model performance in small data regimes by leveraging domain-invariant knowledge learned from a different yet related large source dataset. However, its applicability in trauma mortality prediction is challenging since EHRs collected from different hospital systems encounter feature disparity, i.e. distinct features between the source and target domain data. This paper demonstrates the effectiveness of three DA techniques in trauma mortality prediction, with a private encoding strategy that maps EHRs in both source and target domains with different raw features into the same latent space to alleviate feature disparity issues. Our experimental results on two real-world EHR datasets with various training data scenarios show that DA can improve mortality prediction consistently and significantly with private encoding. Finally, an ablation study manifests the importance of modeling feature disparity in DA, and 2-d t-SNE analysis explains its effectiveness.