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Comparing penalization methods for linear models on large observational health data.

E. Fridgeirsson, R. Williams, P. Rijnbeek, M. Suchard, и J. Reps. J. Am. Medical Informatics Assoc., 31 (7): 1514-1521 (2024)

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Jenna Hildebrandt

Jenna Voss

Markus Reps

Christian Repschläger

Jenna Lüttgen

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Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations.J. Reps, J. Garibaldi, U. Aickelin, J. Gibson, и R. Hubbard. CoRR, (2016)Machine-learning model to predict the cause of death using a stacking ensemble method for observational data.C. Kim, S. You, J. Reps, J. Cheong, и R. Park. J. Am. Medical Informatics Assoc., 28 (6): 1098-1107 (2021)Refining Adverse Drug Reactions Using Association Rule Mining for Electronic Healthcare Data.J. Reps, U. Aickelin, J. Ma, и Y. Zhang. ICDM Workshops, стр. 763-770. IEEE Computer Society, (2014)Comparing penalization methods for linear models on large observational health data.E. Fridgeirsson, R. Williams, P. Rijnbeek, M. Suchard, и J. Reps. J. Am. Medical Informatics Assoc., 31 (7): 1514-1521 (2024)Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasets.J. Reps, P. Ryan, P. Rijnbeek, и M. Schuemie. J. Big Data, 8 (1): 108 (2021)Using simulation to incorporate dynamic criteria into multiple criteria decision-making.U. Aickelin, J. Reps, P. Siebers, и P. Li. CoRR, (2020)The DELPHI Library: Improving Model Validation, Transparency and Dissemination Through a Centralised Library of Prediction Models.R. Williams, S. den Otter, J. Reps, и P. Rijnbeek. MIE, том 302 из Studies in Health Technology and Informatics, стр. 139-140. IOS Press, (2023)Why predicting risk can't identify 'risk factors': empirical assessment of model stability in machine learning across observational health databases.A. Markus, P. Rijnbeek, и J. Reps. MLHC, том 182 из Proceedings of Machine Learning Research, стр. 828-852. PMLR, (2022)Incorporating Spontaneous Reporting System Data to Aid Causal Inference in Longitudinal Healthcare Data.J. Reps, и U. Aickelin. ICDM Workshops, стр. 119-126. IEEE Computer Society, (2014)Does Using a Stacking Ensemble Method to Combine Multiple Base Learners Within a Database Improve Model Transportability?C. Yang, E. Fridgeirsson, J. Kors, J. Reps, P. Rijnbeek, J. Wong, и R. Williams. MIE, том 302 из Studies in Health Technology and Informatics, стр. 129-130. IOS Press, (2023)

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