ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
HIBERT: A Hybrid Clustering BERT for Interpretable Opioid Overdose Risk Prediction.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
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.
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41726407PMC12919516What OpenQuestion holds
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