使用自然语言处理和机器学习来识别电子健康记录数据中的阿片类药物
Sean P McDermott1, Ajay D Wasan1
1Division of Pain Medicine, Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
Journal of pain research
|June 26, 2023
概括
机器学习和自然语言处理在电子健康记录中准确地分类阿片类和非阿片类药物. 这种自动化方法减少了手动审查,并改善了疼痛研究的数据分析.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 药理学 药理学是指药理学的学科.
背景情况:
- 电子健康记录 (EHR) 包含大量的非结构化数据.
- 在EHR中手动审查药物数据是耗时的,容易出现错误.
- 药物的准确分类,如阿片类药物,对于临床研究和患者护理至关重要.
研究的目的:
- 评估机器学习 (ML) 和自然语言处理 (NLP) 在电子健康记录中对药物名称进行分类的有效性.
- 开发和评估一种自动化方法来区分阿片类药物和非阿片类药物.
- 确定ML/NLP在处理EHR数据以进行回顾性分析时的有用性.
主要方法:
- 从EHR中获得的4216个不同的药物条目的数据集被手动标记为阿片类或非阿片类.
- 实施了一种包词NLP方法与监督的ML分类相结合.
- 该模型对60%的数据进行了训练,并对剩余的40%进行了验证.
主要成果:
- 自动分类实现了高性能:准确率为99.6%,灵敏度为97.8%,AUC为0.998.
- 在4216条条目中,225条被归类为阿片类药物 (5.3%),3991条被归类为非阿片类药物 (94.7%).
- 通过大约15-20个阿片类药物和80-100个非阿片类药物训练示例,实现了有效的分类 (准确度,灵敏度,AUC>90-95%).
结论:
- 根据EHR数据,ML/NLP方法在将阿片类药物和非阿片类药物分类方面表现出色.
- 这种自动化方法大大减少了手动查看图表的需要.
- 该方法可以适应更广泛的EHR数据分析和疼痛研究和其他大数据研究中的预测建模.
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