论文标题
深度神经网络，用于过量死亡的细粒度监测
Deep neural networks for fine-grained surveillance of overdose mortality
论文作者
论文摘要
药物过量死亡的监测依赖于死亡证明来识别导致死亡的物质。可以通过国际疾病分类，死亡证明中存在的第10修订（ICD-10）代码来识别毒品和药物类别。但是，ICD-10代码并不总是在药物识别方面提供高水平的特异性。为了在死亡证明书上获得更细粒度的鉴定，必须分析由医疗证明书完成的自由文本死亡部分。当前分析自由文本死亡证明的方法仅依赖于查找表来识别特定物质，必须经常更新和维护。为了改善对死亡证书的药物的识别，开发了一种深入学习命名的识别模型，该模型的F1得分为99.13％。该模型可以识别当前监视查找表中不存在的新药物拼写错误和新型物质，从而增强了药物过量死亡的监视。
Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death. Drugs and drug classes can be identified through the International Classification of Diseases, 10th Revision (ICD-10) codes present on death certificates. However, ICD-10 codes do not always provide high levels of specificity in drug identification. To achieve more fine-grained identification of substances on a death certificate, the free-text cause of death section, completed by the medical certifier, must be analyzed. Current methods for analyzing free-text death certificates rely solely on look-up tables for identifying specific substances, which must be frequently updated and maintained. To improve identification of drugs on death certificates, a deep learning named-entity recognition model was developed, which achieved an F1-score of 99.13%. This model can identify new drug misspellings and novel substances that are not present on current surveillance look-up tables, enhancing the surveillance of drug overdose deaths.
