Dual Ontology-enhanced Clinical Decision Learning for First-admission Mortality Prediction

Time-series based deep learning methods have significantly improved performance of predictive healthcare tasks on electronic health records (EHR) data. However, mortality prediction for first admissions is a huge challenge due to the absence of historical visit sequences. Analysis of major healthcare databases reveals that 55.17% in MIMIC-IV and 83.80% in MIMIC-III present with single visit records only. Many of these single-visit patients are admitted directly to ICU, highlighting the critical need for early prediction models that can inform timely interventions and substantially impact patient outcomes, even without longitudinal history. On first-admission mortality prediction, Dual Ontology-enhanced Clinical Decision learning (DOCD) has been proposed in this paper where we effectively leverage clinical knowledge through two key components. Dual ontology-enhanced learning extracts hierarchical representations from both diagnosis and procedure taxonomies. Furthermore, a priori-guided attention mechanism integrates clinical knowledge through probability based regularization to model the decision-making process. An information fusion is then employed to seamlessly integrate the demographic data, vital signs, and knowledge enhanced medical codes. Extensive experiments on MIMIC III (AUROC: 0.9528, AUPRC: 0.8971) and MIMIC-IV (AUROC: 0.9817, AUPRC: 0.8857) datasets demonstrate DOCD's superior performance over all baselines, offering interpretable visualizations aligned with established clinical knowledge.