深度神经网络用于药物过量死亡率的细粒度监测
Deep neural networks for fine-grained surveillance of overdose mortality
摘要：
对药物过量死亡的监测依赖于死亡证明，以确定导致死亡的物质。药物和药物类别可通过死亡证明上的国际疾病分类第10次修订本（ICD-10）编码来确定。然而，ICD-10编码并不总是在药物识别方面提供高水平的特异性。为了实现对死亡证明上的物质的更细粒度的识别，必须分析由医学证明人完成的死亡原因部分的自由文本。目前用于分析自由文本死亡证明的方法仅依赖于用于识别特定物质的查找表，该查找表必须经常更新和维护。为了提高死亡证明上的药物识别，开发了一种深度学习命名实体识别模型，其F1得分为99.13%。该模型可以识别新的药物拼写错误和当前监测查找表中不存在的新物质，从而加强对药物过量死亡的监测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
