arXiv cs.CL· Raavi Gupta, Ziqing Yang, Karli R. Hochstatter, Talia Nadel, Nicole D'Anna, Jason K. Graham, Hannah Johnson, Kelsey Opozda, Nabila El-Bassel, Anubhav Jangra, Smaranda Muresan·· 3 天前AI 评分22
基于死亡调查报告的证据锚定过量用药死亡预测
Evidence-Anchored Overdose Death Prediction from Death Investigation Reports
AI 导读
研究提出一套自动化框架，可从死亡调查报告中分类疑似过量用药死亡，并生成由专家知识引导的支持性解释。实验显示，采用基于输入报告证据的 pipeline 模型（先解释后预测）优于自解释模型，最佳模型比当前部署的规则系统高出 15%，同时保留专家介入最终决策的空间。
正文 · 原文
Authors:Raavi Gupta, Ziqing Yang, Karli R. Hochstatter, Talia Nadel, Nicole D'Anna, Jason K. Graham, Hannah Johnson, Kelsey Opozda, Nabila El-Bassel, Anubhav Jangra, Smaranda Muresan
Abstract:Confirmation of overdose deaths in the United States is often delayed due to the complexities of certifying drug-involved deaths. Death investigation reports represent a rich but underutilized data source that could be used to predict whether a death is a suspected overdose (i.e., a death preliminarily identified as an overdose prior to the final cause and manner of death determination). We present an automated framework that classifies suspected overdoses from death investigation reports, while producing supporting explanations guided by expert knowledge. Through an expert-guided design process, we investigate 1) what type of explanations are best for model prediction performance, and 2) which architecture is better suited for this task: pipeline models that rationalize first and then predict, or self-rationalizing models that jointly predict and rationalize? Our experiments show that a pipeline model with explanations grounded in the input death investigation reports outperforms the other settings. Our best model also outperforms a rule-based system currently in deployment by 15%, while allowing expert interventions for final decisions.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.19556 [cs.CL] |
| (or arXiv:2610.19556v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19556 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Raavi Gupta [view email]
[v1]
Wed, 14 Oct 2026 04:13:35 UTC (161 KB)
来源：arXiv cs.CL · arxiv.org