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New multi-expert framework improves opioid overdose risk prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed OverdoseMoE, a novel multi-expert framework designed to predict opioid overdose risk using patient diagnostic histories. The framework builds upon Qwen-based models, specifically OODMAMBA and OODQWEN, which were fine-tuned for diagnosis-specific adaptation. OverdoseMoE integrates these models with complementary expert-weighting strategies, demonstrating improved predictive performance over single-model baselines. The system achieved an AUPRC of 25.17 and an AUROC of 69.49 on a MIMIC-IV cohort, showing robustness and enhanced precision in identifying high-risk patients. AI

IMPACT This framework could enhance clinical decision-making by providing more accurate and robust tools for identifying patients at high risk of opioid overdose.

RANK_REASON The cluster describes a research paper published on arXiv detailing a new framework and models for a specific prediction task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New multi-expert framework improves opioid overdose risk prediction

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The cluster describes a research paper published on arXiv detailing a new framework and models for a specific prediction task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mingchen Li, Rohan Pandey, Junhui Qian, Feiyun Ouyang, Sunjae Kwon, Avijit Mitra, Zonghai Yao, Hong Yu ·

    OverdoseMoE: A Multi-Expert Framework for Opioid Overdose Risk Prediction

    arXiv:2609.40108v1 Announce Type: new Abstract: Opioid overdose remains a major clinical and public health burden, highlighting the need for scalable approaches to identify patients at high risk. Here, we investigate diagnosis-specific adaptation for 180-day opioid overdose risk …

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