一种文字挖掘方法,根据药物错误类型对患者安全事件报告进行分类
Christian Boxley1, Mari Fujimoto2, Raj M Ratwani3,4
1MedStar Health National Center for Human Factors in Healthcare, 3007 Tilden St., NW Suite 6N, Washington, DC, 20008, USA. Christian.L.Boxley@medstar.net.
Scientific reports
|October 26, 2023
概括
自然语言处理可以通过对患者安全报告进行分类来提高药物安全性. XGBoost模型最好地识别了药物错误,有助于发现趋势.
科学领域:
- 医疗信息学 医疗信息学
- 医疗信息学 医疗信息学
- 患者安全 患者安全
背景情况:
- 患者安全报告系统捕获药物错误,但往往缺乏分析.
- 未被发现的安全隐患在医疗保健中构成风险.
研究的目的:
- 评估自然语言处理 (NLP) 以改善与药物相关的患者安全事件报告的分类.
- 评估用于识别特定药物错误类型的机器学习算法.
主要方法:
- 利用了3,861份注释的药物安全报告.
- 开发并测试了三个模型:逻辑回归,弹性网和XGBoost.
- 在预定义的药物错误类别中分析模型性能.
主要成果:
- XGBoost模型在分类药物错误方面表现出卓越的性能.
- "错误的药物"",错误的剂量形式或技术或途径"和"不当剂量/错过剂量"是高度可识别的类别.
- 识别了与每个错误类型和共同出现的错误类别相关的关键术语.
结论:
- 机器学习提供了一种半自动化的方法来从自由文本报告中分类药物错误.
- 改进的分类可以提高关键药物安全模式和趋势的识别.
- NLP技术有可能促进药物安全监测.
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