Jenna Reps, PhD
Manager, Epidemiology Analytics
Janssen Research and Development
Jenna Reps is a Senior Epidemiology Informaticist at Janssen research and Development where she is focusing on developing novel solutions to personalise risk prediction. Jenna’s areas of expertise include applying machine learning and data mining techniques to develop solutions for various healthcare problems. She is currently working within the patient level prediction OHDSI workgroup with the aim of developing open source and user friendly software for developing risk models using data sets in the OMOP Common Data Model format.
Prior to joining Janssen Research and Development, Jenna was a Senior Research Fellow at the University of Nottingham where she developed supervised learning techniques to signal adverse drug reactions using UK primary care data and acted as a data consultant to other researchers within the University. Jenna received her BSc in Mathematics and MSc in Mathematical Biology at the University of Bath and her PhD in Computer Science at the University of Nottingham.
Reps, J. M., Schuemie, M. J., Suchard, M. A., Ryan, P. B., & 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 Association, 25(8), 969-975.
Reps, J. M., Rijnbeek, P. R., & Ryan, P. B. (2019). Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data. Drug safety, 1-10.
Reps, J. M., Rijnbeek, P. R., & Ryan, P. B. (2019). Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking status. Journal of biomedical informatics, 103264.
Johnston, S. S., Morton, J. M., Kalsekar, I., Ammann, E. M., Hsiao, C. W., & Reps, J. (2019). Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery. Value in Health, 22(5), 580-586.
Cepeda, M. S., Reps, J., & Ryan, P. (2018). Finding factors that predict treatment‐resistant depression: Results of a cohort study. Depression and anxiety, 35(7), 668-673.
Reps, Jenna; Garibaldi, Jonathan; Aickelin, Uwe; Gibson, Jack; Hubbard, Richard (2015): A Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill’s Causality Considerations. In: Journal of Biomedical Informatics, 56 , pp. 356-368, 2015.
Reps, Jenna; Garibaldi, Jonathan; Aickelin, Uwe; Soria, Daniele; Gibson, Jack; Hubbard, Richard (2014): Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. In: Drug Safety, 37 (3), pp. 163-170, 2014.
