@文章{信息:doi/10.2196/18001，作者="Wheless, Lee和Baker, Laura和Edwards, LaVar和Anand, Nimay和Birdwell, Kelly和Hanlon, Allison和Chren, Mary-Margaret"，标题="开发用于器官移植受者识别的表型算法:队列研究"，期刊="JMIR Med Inform"，年="2020"，月="12"，日="10"，卷="8"，数="12"，页数="e18001"，关键词="表型;电子健康档案;背景:涉及器官移植受者(otr)的研究通常仅限于国家移植受者科学登记数据库中收集的变量。如果能够准确地识别otr，电子健康记录包含额外的变量，可以增加这个数据源。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电子健康记录数据库的未识别版本，其中包含近300万受试者，开发识别otr的算法。我们确定了所有19,817名至少有一个器官移植国际疾病分类(ICD)或当前程序术语(CPT)代码的个体。我们对随机选择的1350名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例回顾患者图表中，827例为器官移植接受者，511例没有移植记录，12例是模棱两可的。 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. ", issn="2291-9694", doi="10.2196/18001", url="http://medinform.www.mybigtv.com/2020/12/e18001/", url="https://doi.org/10.2196/18001", url="http://www.ncbi.nlm.nih.gov/pubmed/33156808" }