识别和量化涉及假冒阿片类止痛产品的暴露
Nancy A West1, Gabrielle E Bau2, Heather Olsen2
1West Research Consulting, Salt Lake City, UT, USA.
Clinical toxicology (Philadelphia, Pa.)
|October 10, 2024
概括
假冒阿片类药物越来越多地影响了阿片类药物流行病. 对中毒中心数据的机器学习分析显示,假冒阿片类药物暴露的嫌疑人显著增加,突出了它们对滥用和滥用趋势的影响.
科学领域:
- 公共卫生 公共卫生
- 毒理学 毒理学 毒理学
- 数据科学数据科学数据科学
背景情况:
- 假冒阿片类药物对公共卫生监测系统构成重大威胁.
- 对假冒阿片类药物产品的准确数据对于了解和应对阿片类药物流行病至关重要.
研究的目的:
- 开发和验证一种新的机器学习方法,以识别和量化涉及疑似假冒阿片类药物产品的暴露.
- 用美国中毒中心数据分析疑似假冒阿片类药物暴露的趋势.
主要方法:
- 一项生态研究利用了2009年至2021年间的研究滥用,转移和成相关监控系统 (RADARS) 的数据.
- 开发了一种机器学习自然语言处理 (NLP) 模型,用于分析来自毒药中心电话的叙事案例笔记.
主要成果:
- 该NLP模型在检测涉嫌假冒的曝光时显示出高灵敏度 (92%) 和特异性 (73%).
- 虽然从2009年到2021年,预计只有2.1%的电话涉及假冒,但观察到显著的指数级增长,2021年有23.7%的阿片类止痛药暴露被怀疑涉及假冒.
- 在2009年至2021年期间,估计的假冒嫌疑人曝光率增加了7倍.
结论:
- 机器学习NLP是一种可行和可靠的方法,用于识别在中毒中心数据中的可疑假冒阿片类药物暴露.
- 疑似伪造的阿片类药物在近年来对阿片类止痛药的故意滥用和滥用率产生了重大影响.
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