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Increasing the efficiency of trial-patient matching: automated clinical trial eligibility Pre-screening for pediatric oncology patients.

, , , , , , , and . BMC Med. Inf. & Decision Making, (2015)

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Leveraging Noisy Online Databases for Use in Chord Recognition., , , and . ISMIR, page 639-644. University of Miami, (2011)An End-to-End Machine Learning System for Harmonic Analysis of Music., , , and . IEEE Trans. Audio, Speech & Language Processing, 20 (6): 1771-1783 (2012)Will they participate? Predicting patients' response to clinical trial invitations., , , , , and . AMIA, AMIA, (2015)Will they participate? Predicting patients' response to clinical trial invitations in a pediatric emergency department., , , , , , and . JAMIA, 23 (4): 671-680 (2016)Meta-song evaluation for chord recognition, , , and . CoRR, (2011)Exploiting Long-Range Dependencies in Protein beta-Sheet Secondary Structure Prediction., and . PRIB, volume 6282 of Lecture Notes in Computer Science, page 349-357. Springer, (2010)Desiderata for computable representations of electronic health records-driven phenotype algorithms., , , , , , , , , and 19 other author(s). J. Am. Medical Informatics Assoc., 22 (6): 1220-1230 (2015)Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department., , , , , , , , and . JAMIA, 22 (1): 166-178 (2015)An end-to-end hybrid algorithm for automated medication discrepancy detection., , , , , , , , and . BMC Med. Inf. & Decision Making, (2015)Increasing the efficiency of trial-patient matching: automated clinical trial eligibility Pre-screening for pediatric oncology patients., , , , , , , and . BMC Med. Inf. & Decision Making, (2015)