Identifying naloxone administrations in electronic health record data using a text-mining tool.
Identifying naloxone administrations in electronic health record data using a text-mining tool.
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DOI：
10.1080/08897077.2020.1856288
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发表时间：
2021
期刊：
影响因子：
3.5
通讯作者：
Binswanger IA
中科院分区：
文献类型：
--
作者：
Derington CG;Mueller SR;Glanz JM;Binswanger IA
需要有效和高效的方法来识别电子健康记录(EHR)数据中的纳洛酮给药，以进行过量监测和研究。这项研究的目的是开发和验证文本挖掘工具，以识别电子病历数据中的纳洛酮给药。2017年1月至2018年3月期间存储在数据库中的临床笔记被用于迭代开发文本挖掘工具，以识别纳洛酮给药。该工具的第一次迭代使用了广泛的搜索词。然后，在回顾了过量用药的临床记录后，我们开发了一份描述纳洛酮给药的短语列表，以告知第二次迭代。在验证迭代2的同时，发现了其他短语，然后添加这些短语以通知最终迭代。比较器是从《电子健康记录》中摘录的行政代码查询。病历回顾被用来确定真正的阳性。主要结果是第二次迭代、最终迭代和管理代码查询的积极预测值(PPV)。迭代2、最终迭代和管理代码的PPV分别为84.3%(95%可信区间78.6-89.0%)、83.8%(95%可信区间78.6-88.2%)和57.1%(95%可信区间47.1-66.8%)。该工具的两次迭代的PPV都显著高于管理代码(P<；0.001)。一个文本挖掘工具将电子病历数据中纳洛酮给药的识别率从管理代码的不到60%提高到该工具的两个版本的80%以上。文本挖掘工具可以为更复杂的信息学方法的使用提供信息，这通常需要大量的时间、资源和专业知识投资。
Effective and efficient methods are needed to identify naloxone administrations within electronic health record (EHR) data to conduct overdose surveillance and research. The objective of this study was to develop and validate a text-mining tool to identify naloxone administrations in EHR data. Clinical notes stored in databases between January 2017 and March 2018 were used to iteratively develop a text-mining tool to identify naloxone administrations. The first iteration of the tool used broad search terms. Then, after reviewing clinical notes of overdose encounters, we developed a list of phrases that described naloxone administrations to inform iteration two. While validating iteration two, additional phrases were found, which were then added to inform the final iteration. The comparator was an administrative code query extracted from the EHR. Medical record review was used to identify true positives. The primary outcome was the positive predictive values (PPV) of the second iteration, final iteration, and administrative code query. Iteration two, the final iteration, and the administrative code had PPVs of 84.3% (95% confidence interval [CI] 78.6–89.0%), 83.8% (95% CI 78.6–88.2%), and 57.1% (95% CI 47.1–66.8%), respectively. Both iterations of the tool had a significantly higher PPV than the administrative code (P<0.001). A text-mining tool improved the identification of naloxone administrations in EHR data from less than 60% with the administrative code to greater than 80% with both versions of the tool. Text-mining tools can inform the use of more sophisticated informatics methods, which often require significant time, resource, and expertise investment.
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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：
2.7
作者：
Lim, Dennison;Melucci, Joe;Weber, Robert J.
通讯作者：
Weber, Robert J.
电子健康记录的文本挖掘经验进步。
影响因子：
3.5
作者：
Delespierre T;Denormandie P;Bar-Hen A;Josseran L
通讯作者：
Josseran L
DOI：
10.1093/jamia/ocu041
发表时间：
2015-05
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Nikfarjam A;Sarker A;O'Connor K;Ginn R;Gonzalez G
通讯作者：
Gonzalez G
影响因子：
6.2
作者：
Beaudoin, Francesca L.;Merchant, Roland C.;Babu, Kavita M.
通讯作者：
Babu, Kavita M.