A text mining approach to categorize patient safety event reports by medication error type.
A text mining approach to categorize patient safety event reports by medication error type.
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DOI：
10.1038/s41598-023-45152-w
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发表时间：
2023-10-26
影响因子：
4.6
通讯作者：
Fong, Allan
中科院分区：
文献类型：
--
作者：
Boxley, Christian;Fujimoto, Mari;Ratwani, Raj M.;Fong, Allan
患者安全报告系统使医疗保健提供者工作人员能够报告与药物相关的安全事件和错误；然而，这些报告中的许多没有得到分析，安全风险也没有被发现。这项研究的目的是检验是否可以使用自然语言处理来更好地对与药物相关的患者安全事件报告进行分类。使用先前使用合并用药差错分类法标注的3861份与用药相关的患者安全事件报告，使用以下算法来建立三个模型：(1)Logistic回归，(2)弹性网络，(3)XGBoost。开发完成后，对模型进行了测试，并对模型性能进行了分析。我们发现XGBoost模型在所有用药错误类别中表现最好。在这三个模型中，“Wong药物”、“错误的剂型或技术或路线”以及“不适当的剂量/剂量遗漏”类别的表现最好。此外，我们确定了与每个用药差错类别最密切相关的五个单词，以及哪些用药差错类别最有可能同时出现。机器学习技术提供了一种半自动方法，用于从患者安全事件报告的免费文本中识别特定的用药错误类型。这些算法有可能改进与药物相关的患者安全事件报告的分类，这可能导致更好地识别重要的药物安全模式和趋势。
Patient safety reporting systems give healthcare provider staff the ability to report medication related safety events and errors; however, many of these reports go unanalyzed and safety hazards go undetected. The objective of this study is to examine whether natural language processing can be used to better categorize medication related patient safety event reports. 3,861 medication related patient safety event reports that were previously annotated using a consolidated medication error taxonomy were used to develop three models using the following algorithms: (1) logistic regression, (2) elastic net, and (3) XGBoost. After development, models were tested, and model performance was analyzed. We found the XGBoost model performed best across all medication error categories. ‘Wrong Drug’, ‘Wrong Dosage Form or Technique or Route’, and ‘Improper Dose/Dose Omission’ categories performed best across the three models. In addition, we identified five words most closely associated with each medication error category and which medication error categories were most likely to co-occur. Machine learning techniques offer a semi-automated method for identifying specific medication error types from the free text of patient safety event reports. These algorithms have the potential to improve the categorization of medication related patient safety event reports which may lead to better identification of important medication safety patterns and trends.
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影响因子：
2.6
作者：
Chang, A;Schyve, PM;Loeb, JM
通讯作者：
Loeb, JM
影响因子：
2.2
作者：
Adams, Katharine T.;Pruitt, Zoe;Ratwani, Raj M.
通讯作者：
Ratwani, Raj M.
影响因子：
4.5
作者：
Sevenster, Merlijn;Bozeman, Jeffrey;Trost, William
通讯作者：
Trost, William
DOI：
10.1197/jamia.m3028
发表时间：
2009-05-01
影响因子：
6.4
作者：
Wang, Xiaoyan;Hripcsak, George;Friedman, Carol
通讯作者：
Friedman, Carol
影响因子：
3.4
作者：
Agrawal, Abha
通讯作者：
Agrawal, Abha