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However, assessing a patient's phenotype is costly and labor intensive, hence a proper selection of EHR records as a training set is desired. We propose a procedure to tailor the best training subsample with limited sample size for a classification model, minimizing its mean-squared phenotyping\/classification error (MSE). Our approach incorporates \u201cpositive only\u201d information, an approximation of the true disease status without false alarm, when it is available. In addition, our sampling procedure is applicable for training a chosen classification model which can be misspecified. We provide theoretical justification on its optimality in terms of MSE. The performance gain from our method is illustrated through simulation and a real-data example, and is found often satisfactory under criteria beyond MSE.<\/jats:p>","DOI":"10.1111\/biom.13824","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T07:08:27Z","timestamp":1673507307000},"page":"2974-2986","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimal Sampling for Positive Only Electronic Health Record Data"],"prefix":"10.1093","volume":"79","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7320-6788","authenticated-orcid":false,"given":"Seong-H.","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Statistics, Pennsylvania State University , State College, Pennsylvania , 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