HIBERT: 解釈可能なオピオイド過剰摂取リスク予測のためのハイブリッドクラスタリングBERT

Drug overdose, mostly from opioids, is a continuing crisis in the US. Highly accurate models for early detection of opioid overdose (OD) risk are crucial for early intervention and prevention. While deep learning has shown promise in using electronic health records (EHRs) for OD risk prediction, its clinical utility is often limited by challenges with data sparsity, heterogeneity, label imbalance, and lack of interpretability. We present HIBERT, a hybrid BERT model that combines the transformer model with deep clustering. HIBERT uses a multiple BERT architecture integrating specialized BERT modules for distinct EHR feature categories, and incorporates deep significance clustering to generate clinically meaningful risk stratification. HIBERT outperforms conventional and state-of-the-art models based on evaluation with the Health Facts database and identifies four distinct risk clusters, in addition to ranked critical features. It provides actionable, personalized OD risk assessment with improved interpretability.