器官移植受者表型识别算法的研究进展卡塔尔世界杯8强波胆分析队列研究%A Wheless,Lee %A Baker,Laura %A Edwards,LaVar %A Anand,Nimay %A Birdwell,Kelly %A Hanlon,Allison %A Chren,Mary-Margaret %+范德比尔特大学医学中心皮肤科，719 Thompson Lane, Suite 26300, Nashville, TN, 37204，美国，1 6153226485,lee.e.wheless@vumc.org %K表型%K电子健康记录%K器官移植受者%D 2020 %7 10.12.2020 %9原始论文%J JMIR Med Inform %G English %X涉及器官移植受者(OTRs)的研究通常局限于国家移植受者科学登记数据库中收集的变量。电子健康记录包含额外的变量，如果可以准确地识别otr，这些变量可以增强该数据源。目的:本研究的目的是开发从电子健康记录中识别otr的表型算法。方法:我们使用范德比尔特大学的电子健康记录数据库的去识别版本，其中包含近300万受试者，来开发识别otr的算法。我们确定了所有19,817例患者至少具有一种器官移植国际疾病分类(ICD)或现行程序术语(CPT)代码。我们对1350名随机选择的个体进行了图表回顾，以确定移植状态。我们构建了机器学习模型，通过使用分类和回归树、随机森林和极端梯度增强算法来计算代码组合的正预测值和灵敏度。结果:在1350例病例中，827例为器官移植接受者，511例无移植记录，12例不明确。 Most patients with only 1 or 2 transplant codes did not have a transplant. The most common reasons for being labeled a nontransplant patient were the lack of data (229/511, 44.8%) or the patient being evaluated for an organ transplant (174/511, 34.1%). All 3 machine learning algorithms identified OTRs with overall >90% positive predictive value and >88% sensitivity. Conclusions: Electronic health records linked to biobanks are increasingly used to conduct large-scale studies but have not been well-utilized in organ transplantation research. We present rigorously evaluated methods for phenotyping OTRs from electronic health records that will enable the use of the full spectrum of clinical data in transplant research. Using several different machine learning algorithms, we were able to identify transplant cases with high accuracy by using only ICD and CPT codes. %M 33156808 %R 10.2196/18001 %U http://medinform.www.mybigtv.com/2020/12/e18001/ %U https://doi.org/10.2196/18001 %U http://www.ncbi.nlm.nih.gov/pubmed/33156808

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