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A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn\u2019s disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100\u2013300, with no statistically significant difference.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level \u2013 phenotypic big data.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocx111","type":"journal-article","created":{"date-parts":[[2017,9,14]],"date-time":"2017-09-14T11:13:31Z","timestamp":1505387611000},"page":"54-60","source":"Crossref","is-referenced-by-count":110,"title":["Enabling phenotypic big data with PheNorm"],"prefix":"10.1093","volume":"25","author":[{"given":"Sheng","family":"Yu","sequence":"first","affiliation":[{"name":"Center for Statistical Science, Tsinghua University, Beijing, China"},{"name":"Department of Industrial Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumeng","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Gronsbell","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Harvard T.H. 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