两种用户友好的方法的比较,以识别和支持纠正错误拼写的药物
Christopher R Dasaro1, Ahmad Sabra1, Yunho Jeon1
1World Trade Center Health Program General Responder Data Center, Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, 17 East 102 Street, 2 Floor, New York, NY 10029, United States of America.
Preventive medicine reports
|May 27, 2024
概括
两种方法有效地识别了在自由文本记录中拼写错误的药物名称. 模糊字符串搜索和拼写检查器都尽量减少了手动审查,模糊方法提供了更广泛的适用性.
科学领域:
- 医疗信息学 医疗信息学
- 药物监督 药物监督 药物监督
- 数据科学数据科学数据科学
背景情况:
- 准确的药物记录对于患者安全和有效的药物监测至关重要.
- 药物的自由文本条目容易出现拼写错误,使数据分析和临床审查复杂化.
- 需要自动化方法来识别和纠正错误拼写的药物名称,而不需要依赖临床专业知识.
研究的目的:
- 为了比较两种用户友好的方法在自由文本电子健康记录中识别和纠正拼写错误的药物名称的有效性.
- 为了评估模糊字符串搜索算法的性能,与用于药物名称纠正的商业拼写检查器对比.
主要方法:
- 利用SAS® COMPGED功能和模糊字符串搜索对来自世界贸易中心总响应者队伍的180万种药物记录.
- 应用了广义的编辑距离得分值 (<120) 来识别潜在的拼写错误.
- 将模糊的搜索结果与Stedman的2020 Plus医疗/制药拼写检查器进行比较,评估协议,错误阳性和错误负面.
主要成果:
- 在识别各种治疗类 (如焦虑症,喘,GERD) 中正确拼写的药物的高准确性.
- 这两种方法都显示了高的同意率 (例如,焦虑药物的99.9%),低的假阳性率.
- 模糊搜索偶尔会发现拼写检查器错过的药物,这表明它对特定情况下的实用性.
结论:
- 模糊字符串搜索和拼写检查都有效地识别了拼写错误的药物,大大减少了手动审查的需求.
- 模糊方法广泛适用于特定疾病的药物识别,但需要编程专业知识.
- 拼写检查器具有成本效益,但具有语言限制,需要一些编程技能.
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