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Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.

, , , , , , , , and . JAMIA, 22 (5): 993-1000 (2015)

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Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources., , , , , , , , and . JAMIA, 22 (5): 993-1000 (2015)EXTraction of EMR numerical data: an efficient and generalizable tool to EXTEND clinical research., , , , , , and . BMC Medical Informatics Decis. Mak., 19 (1): 226:1-226:7 (2019)Surrogate-assisted feature extraction for high-throughput phenotyping., , , , , , , , , and 1 other author(s). JAMIA, 24 (e1): e143-e149 (2017)Portability of an algorithm to identify rheumatoid arthritis in electronic health records., , , , , , , , , and 10 other author(s). JAMIA, (2012)Using PheWAS to Assess Pleiotropy of Genetic Risk Scores for Rheumatoid Arthritis and Coronary Artery Disease in the eMERGE Network., , , , , , , , , and 7 other author(s). AMIA, AMIA, (2012)Enabling phenotypic big data with PheNorm., , , , , , , , , and 2 other author(s). JAMIA, 25 (1): 54-60 (2018)Automated grouping of medical codes via multiview banded spectral clustering., , , , , , , , , and 3 other author(s). J. Biomed. Informatics, (2019)PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies., , , , , , and . J. Am. Medical Informatics Assoc., 25 (10): 1359-1365 (2018)Demonstrating the Advantages of Applying Data Mining Techniques on Time-Dependent Electronic Medical Records., , , , , , , , , and 8 other author(s). AMIA, AMIA, (2015)Improving EHR Chart Review Efficiency via Semantic Similarity Assessment., , , , , , , , , and . AMIA, AMIA, (2017)