Jenna Marie Reps - researchr alias

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2024

Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health dataCynthia Yang, Egill A. Fridgeirsson, Jan A. Kors, Jenna Marie Reps, Peter R. Rijnbeek. jbd, 11(1):7, December 2024. [doi]

2023

The DELPHI Library: Improving Model Validation, Transparency and Dissemination Through a Centralised Library of Prediction ModelsRoss D. Williams, Sicco den Otter, Jenna Marie Reps, Peter R. Rijnbeek. mie 2023: 139-140 [doi]

Does Using a Stacking Ensemble Method to Combine Multiple Base Learners Within a Database Improve Model Transportability?Cynthia Yang, Egill A. Fridgeirsson, Jan A. Kors, Jenna Marie Reps, Peter R. Rijnbeek, Jenna Wong, Ross D. Williams. mie 2023: 129-130 [doi]

2022

2021

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health dataSara Khalid, Cynthia Yang, Clair Blacketer, Talita Duarte-Salles, Sergio Fernández-Bertolín, Chungsoo Kim, Rae Woong Park, Jimyung Park, Martijn J. Schuemie, Anthony G. Sena, Marc A. Suchard, Seng Chan You, Peter R. Rijnbeek, Jenna Marie Reps. cmpb, 211:106394, 2021. [doi]

Machine-learning model to predict the cause of death using a stacking ensemble method for observational dataChungsoo Kim, Seng Chan You, Jenna Marie Reps, Jae Youn Cheong, Rae Woong Park. jamia, 28(6):1098-1107, 2021. [doi]

Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasetsJenna Marie Reps, Patrick B. Ryan, Peter R. Rijnbeek, Martijn J. Schuemie. jbd, 8(1):108, 2021. [doi]

An empirical analysis of dealing with patients who are lost to follow-up when developing prognostic models using a cohort designJenna Marie Reps, Peter R. Rijnbeek, Alana Cuthbert, Patrick B. Ryan, Nicole Pratt, Martijn J. Schuemie. midm, 21(1):43, 2021. [doi]

2019

Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking statusJenna Marie Reps, Peter R. Rijnbeek, Patrick B. Ryan. jbi, 97, 2019. [doi]

2018

Using simulation to incorporate dynamic criteria into multiple criteria decision-makingUwe Aickelin, Jenna Marie Reps, Peer-Olaf Siebers, Peng Li. jors, 69(7):1021-1032, 2018. [doi]

2016

Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern miningJenna Marie Reps, Uwe Aickelin, Richard B. Hubbard. cbm, 69:61-70, 2016. [doi]

2015

A supervised adverse drug reaction signalling framework imitating Bradford Hill's causality considerationsJenna Marie Reps, Jonathan M. Garibaldi, Uwe Aickelin, Jack E. Gibson, Richard B. Hubbard. jbi, 56:356-368, 2015. [doi]

2014

Detecting adverse drug reactions in the general practice healthcare databaseJenna Marie Reps. PhD thesis, University of Nottingham, UK, 2014. [doi]

Tuning a multiple classifier system for side effect discovery using genetic algorithmsJenna Marie Reps, Uwe Aickelin, Jonathan M. Garibaldi. cec 2014: 910-917 [doi]

Incorporating Spontaneous Reporting System Data to Aid Causal Inference in Longitudinal Healthcare DataJenna Marie Reps, Uwe Aickelin. icdm 2014: 119-126 [doi]

A Novel Semisupervised Algorithm for Rare Prescription Side Effect DiscoveryJenna Marie Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard. titb, 18(2):537-547, 2014. [doi]

Refining Adverse Drug Reactions Using Association Rule Mining for Electronic Healthcare DataJenna Marie Reps, Uwe Aickelin, Jiangang Ma, Yanchun Zhang. icdm 2014: 763-770 [doi]

2013

Comparison of algorithms that detect drug side effects using electronic healthcare databasesJenna Marie Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard. soco, 17(12):2381-2397, 2013. [doi]

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