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Yu, Sheng

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Yu

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Sheng

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Yu, Sheng
Author's Publications: search.filters.isAuthorOfPublication.3830a2b6-593d-4d6f-9f3d-e1e41a943bea

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    Identification of subjects with polycystic ovary syndrome using electronic health records
    (BioMed Central, 2015) Castro, Victor; Shen, Yuanyuan; Yu, Sheng; Finan, Sean; Pau, Cindy Ta; Gainer, Vivian; Keefe, Candace C.; Savova, Guergana; Murphy, Shawn; Cai, Tianxi; Welt, Corrine K.
    Background: Polycystic ovary syndrome (PCOS) is a heterogeneous disorder because of the variable criteria used for diagnosis. Therefore, International Classification of Diseases 9 (ICD-9) codes may not accurately capture the diagnostic criteria necessary for large scale PCOS identification. We hypothesized that use of electronic medical records text and data would more specifically capture PCOS subjects. Methods: Subjects with PCOS were identified in the Partners Healthcare Research Patients Data Registry by searching for the term “polycystic ovary syndrome” using natural language processing (n = 24,930). A training subset of 199 identified charts was reviewed and categorized based on likelihood of a true Rotterdam PCOS diagnosis, i.e. two out of three of the following: irregular menstrual cycles, hyperandrogenism and/or polycystic ovary morphology. Data from the history, physical exam, laboratory and radiology results were codified and extracted from notes of definite PCOS subjects. Thirty-two terms were used to build an algorithm for identifying definite PCOS cases and applied to the rest of the dataset. The positive predictive value cutoff was set at 76.8 % to maximize the number of subjects available for study. A true positive predictive value for the algorithm was calculated after review of 100 charts from subjects identified as definite PCOS cases with at least two documented Rotterdam criteria. The positive predictive value was compared to that calculated using 200 charts identified using the ICD-9 code for PCOS (256.4; n = 13,670). In addition, a cohort of previously recruited PCOS subjects was submitted for algorithm validation. Results: Chart review demonstrated that 64 % were confirmed as definitely PCOS using the algorithm, with a 9 % false positive rate. 66 % of subjects identified by ICD-9 code for PCOS could be confirmed as definitely PCOS, with an 8.5 % false positive rate. There was no significant difference in the positive predictive values using the two methods (p = 0.2). However, the number of charts that had insufficient confirmatory data was lower using the algorithm (5 % vs 11 %; p < 0.04). Of 477 subjects with PCOS recruited and examined individually and present in the database as patients, 451 were found within the algorithm dataset. Conclusions: Extraction of text parameters along with codified data improves the confidence in PCOS patient cohorts identified using the electronic medical record. However, the positive predictive value was not significantly different when using ICD-9 codes or the specific algorithm. Further studies are needed to determine the positive predictive value of the two methods in additional electronic medical record datasets. Electronic supplementary material The online version of this article (doi:10.1186/s12958-015-0115-z) contains supplementary material, which is available to authorized users.
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    Publication
    High-Throughput Phenotyping With Electronic Medical Record Data Using a Common Semi-Supervised Approach (PheCAP)
    (Springer Science and Business Media LLC, 2019-11-20) Zhang, Yichi; Cai, Tianrun; Yu, Sheng; Cho, Kelly; Hong, Chuan; Sun, Jiehuan; Huang, Jie; Xia, Zongqi; Castro, Victor; Gagnon, David; Savova, Guergana; Churchill, Susanne; Gaziano, John; Kohane, Isaac; Cai, Tianxi; Ho, Yuk-Lam; Ananthakrishnan, Ashwin; Shaw, Stanley; Gainer, Vivian; Link, Nicholas; Honerlaw, Jacqueline; Huong, Sicong; Karlson, Elizabeth; Plenge, Robert; Szolovits, Peter; O'Donnell, Christopher; Murphy, Shawn; Liao, Katherine
    Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping using EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures reducing the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 days if all data are available; however, the timing is largely dependent on the chart review step which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes/no).