超越Phecodes:利用PheMAP识别电子健康记录中缺乏诊断代码的患者
Chao Yan1, Monika E Grabowska1, Rut Thakkar1
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
概括
PheMAP有效地识别了缺少电子健康记录诊断代码的患者进行研究. 这提高了糖尿病和痴呆症等疾病的表型准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床研究 临床研究
- 健康 数据科学 数据科学
背景情况:
- 电子健康记录 (EHR) 诊断代码对于研究中的患者表型化至关重要.
- 不完整或不准确的EHR诊断会导致错误的阴性结果,阻碍研究.
- 现有的方法往往错过了具有相关表型但缺乏特定诊断代码的患者.
研究的目的:
- 评估PheMAP,一个整合多种临床术语的知识库,用于识别缺乏EHR诊断代码的患者.
- 评估PheMAP在捕获传统诊断代码之外的表型方面的有效性.
- 提高生物医学研究患者表型的准确性和可靠性.
主要方法:
- 利用了来自范德比尔特大学医学中心EHR的350万名患者记录.
- 应用了PheMAP来匹配结构化概念,并计算了表型风险得分 (PheScore).
- 确定了具有高PheScores但缺少诊断代码的患者;专家判断证实了研究结果.
主要成果:
- 确定了缺乏2型糖尿病 (5.3%),痴呆 (4.5%),前列腺癌 (2.2%) 和感觉神经听力损失 (0.2%) 的诊断代码的患者.
- 在鉴定患者的专家验证中实现了高精度率 (85.0%-100%).
- 证明了PheMAP能够发现标准诊断代码搜索遗漏的表型的能力.
结论:
- 通过整合不同的临床术语,PheMAP提高了表型的质量.
- 这种方法有效地识别了缺乏特定EHR诊断代码的表型患者.
- 通过PheMAP提高表型精度,提高了生物医学研究的可靠性.
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