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Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations., , , , and . CoRR, (2016)Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data., , , , and . J. Am. Medical Informatics Assoc., 25 (8): 969-975 (2018)Machine-learning model to predict the cause of death using a stacking ensemble method for observational data., , , , and . J. Am. Medical Informatics Assoc., 28 (6): 1098-1107 (2021)Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking status., , and . J. Biomed. Informatics, (2019)A supervised adverse drug reaction signalling framework imitating Bradford Hill's causality considerations., , , , and . J. Biomed. Informatics, (2015)Refining Adverse Drug Reactions Using Association Rule Mining for Electronic Healthcare Data., , , and . ICDM Workshops, page 763-770. IEEE Computer Society, (2014)Comparison of algorithms that detect drug side effects using electronic healthcare databases., , , , , and . Soft Comput., 17 (12): 2381-2397 (2013)How little data do we need for patient-level prediction?, , , , and . CoRR, (2020)Does Using a Stacking Ensemble Method to Combine Multiple Base Learners Within a Database Improve Model Transportability?, , , , , , and . MIE, volume 302 of Studies in Health Technology and Informatics, page 129-130. IOS Press, (2023)Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability., , , , and . BMC Medical Informatics Decis. Mak., 22 (1): 142 (2022)