使用自然语言处理技术检测与阿片类药物相关的异常行为。
Detecting Opioid-Related Aberrant Behavior using Natural Language Processing.
作者信息
Lingeman Jesse M, Wang Priscilla, Becker William, Yu Hong
机构信息
University of Massachusetts: Amherst, Amherst, MA.
Yale Medical School, New Haven, CT.
出版信息
AMIA Annu Symp Proc. 2018 Apr 16;2017:1179-1185. eCollection 2017.
The United States is in the midst of a prescription opioid epidemic, with the number of yearly opioid-related overdose deaths increasing almost fourfold since 2000. To more effectively prevent unintentional opioid overdoses, the medical profession requires robust surveillance tools that can effectively identify at-risk patients. Drug-related aberrant behaviors observed in the clinical context may be important indicators of patients at risk for or actively abusing opioids. In this paper, we describe a natural language processing (NLP) method for automatic surveillance of aberrant behavior in medical notes relying only on the text of the notes. This allows for a robust and generalizable system that can be used for high volume analysis of electronic medical records for potential predictors of opioid abuse.
美国正处于处方阿片类药物流行之中，自2000年以来，每年与阿片类药物相关的过量死亡人数几乎增加了四倍。为了更有效地预防意外阿片类药物过量，医疗行业需要强大的监测工具，能够有效地识别高危患者。在临床环境中观察到的与药物相关的异常行为可能是有阿片类药物滥用风险或正在滥用阿片类药物的患者的重要指标。在本文中，我们描述了一种自然语言处理（NLP）方法，用于仅依靠医疗记录文本自动监测医疗记录中的异常行为。这使得能够建立一个强大且可推广的系统，可用于对电子病历进行大量分析，以寻找阿片类药物滥用的潜在预测因素。
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