Subpopulation-Specific Synthetic EHR for Better Mortality Prediction

arXiv:2305.16363v2 Announce Type: replace
Abstract: Electronic health records (EHR) often contain different rates of representation of certain subpopulations (SP). Factors like patient demographics, clinical condition prevalence, and medical center type contribute to this underrepresentation. Consequently, when training machine learning models on such datasets, the models struggle to generalize well and perform poorly on underrepresented SPs. To address this issue, we propose a novel ensemble framework that utilizes generative models. Specifically, we train a GAN-based synthetic data generator for each SP …

abstract arxiv center clinical cs.lg datasets demographics ehr electronic electronic health records health machine machine learning machine learning models medical mortality patient prediction records representation struggle synthetic training type underrepresentation