A multi-domain EHR foundation model for predicting Hepatitis B liver disease: a clinical perspective

Foundation models trained on patient electronic health records (EHRs) hold promise for transforming clinical care by enabling effective decision support and personalized healthcare delivery but have been limited by a focus on intensive care objectives. Here we present a multi-domain transformer-based EHR foundation model designed to predict two liver disease outcomes in patients with Chronic Hepatitis B, an infection characterized by diverse and uncertain medical trajectories. Through case studies employing attention maps, we demonstrate that the transformer model identifies patterns similar to one-liners employed by clinical staff and depends on distinct clinical events to estimate disease progression. Our findings underscore both the utility and challenges of EHR foundation models in clinical care and the necessity to evaluate EHR-models on less-regimented diseases.