Jenna Reps, PhD

Assistant professor of Medical Informatics

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Jenna Reps holds a PhD (University of Nottingham 2014) in Computer science, a MSc (University of Bath 2010) in Mathematical Biology and a BSc (University of Bath 2009) in Mathematics. Jenna’s PhD work focused on developing novel data mining solutions to identify adverse drug reactions in a large UK general practice database.

After her PhD Jenna was a researcher and lecturer at the University of Nottingham and then joined Janssen R&D in 2016.  Jenna Reps has over a decade of experience developing prediction models using large observational databases.  She is an active collaborator in the OHDSI (ohdsi.org) community, co-leads the PatientLevelPrediction workgroup and maintains a number of OHDSI R packages (https://github.com/OHDSI/PatientLevelPrediction , https://github.com/OHDSI/EnsemblePatientLevelPrediction  , https://github.com/OHDSI/OhdsiShinyModules ).  Her current focus is on researching best practices for prediction model development in large observational data and investigating ways to improve model transportability.

In the Health Data Science group Jenna is working on 1) large scale empirical investigations into the impacts of model design choices, 2) ways to improve model transportability and 3) counterfactual prediction methods. 


Reps, J.M., Williams, R.D., Schuemie, M.J., Ryan, P.B. and Rijnbeek, P.R., 2022. Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability. BMC medical informatics and decision making22(1), pp.1-14.

Reps, J.M., Ryan, P.B., Rijnbeek, P.R. and Schuemie, M.J., 2021. Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasets. Journal of Big Data8(1), pp.1-18.

Reps, J.M., Ryan, P. and Rijnbeek, P.R., 2021. Investigating the impact of development and internal validation design when training prognostic models using a retrospective cohort in big US observational healthcare data. BMJ open11(12), p.e050146.

Reps, J.M., Rijnbeek, P., Cuthbert, A., Ryan, P.B., Pratt, N. and Schuemie, M., 2021. An empirical analysis of dealing with patients who are lost to follow-up when developing prognostic models using a cohort design. BMC medical informatics and decision making21(1), pp.1-24.

Reps, J.M., Williams, R.D., You, S.C., Falconer, T., Minty, E., Callahan, A., Ryan, P.B., Park, R.W., Lim, H.S. and Rijnbeek, P., 2020. Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke in female patients newly diagnosed with atrial fibrillation. BMC medical research methodology20(1), pp.1-10.

Reps, J.M., Schuemie, M.J., Suchard, M.A., Ryan, P.B. and Rijnbeek, P.R., 2018. Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. Journal of the American Medical Informatics Association25(8), pp.969-975.

Reps, J.M., Garibaldi, J.M., Aickelin, U., Gibson, J.E. and Hubbard, R.B., 2015. A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations. Journal of Biomedical Informatics56, pp.356-368.