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Dissemination

Key methodological publications led or co-authored by the Foundational Methods pillar that form the basis of our current and upcoming work.

2025

  • Finding a constrained number of predictor phenotypes for multiple outcome prediction. Reps JM, Wong J, Fridgeirsson EA, Kim C, John LH, Williams RD, Fisher RR, Ryan PB, Rijnbeek PR. BMJ Health Care Inform. 2025. doi.org/10.1136/bmjhci-2024-101227.

2024

  • Comparing penalization methods for linear models on large observational health data. Fridgeirsson EA, Williams RD, Rijnbeek PR, Suchard MA, Reps JM. J Am Med Inform Assoc. 2024. doi.org/10.1093/jamia/ocae109.
  • Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data. Yang C, Fridgeirsson EA, Kors JA, Reps JM, Rijnbeek PR J Big Data. 2024. doi.org/10.1186/s40537-023-00857-7.
  • Health-Analytics Data to Evidence Suite (HADES): open-source software for observational research. Schuemie M, Reps J, Black A, Defalco F, Evans L, Fridgeirsson E, Gilbert JP, Knoll C, Lavallee M, Rao GA, Rijnbeek P, Sadowski K, Sena A, Swerdel J, Williams RD, Suchard M. Stud Health Technol Inform. 2024. doi.org/10.3233/SHTI231108.
  • Comparison of deep learning and conventional methods for disease onset prediction. John LH, Kim C, Kors JA, Chang J, Morgan-Cooper H, Desai P, Pang C, Rijnbeek PR, Reps JM, Fridgeirsson EA. arXiv. 2024. doi.org/10.48550/arXiv.2410.10505. [Preprint]

2023

  • Attention-based neural networks for clinical prediction modelling on electronic health records. Fridgeirsson EA, Sontag D, Rijnbeek PR. BMC Med Res Methodol. 2023. doi.org/10.1186/s12874-023-02112-2.

2022

  • Logistic regression models for patient-level prediction based on massive observational data: do we need all data? John LH, Kors JA, Reps JM, Ryan PB, Rijnbeek PR. Int J Med Inform. 2022. doi.org/10.1016/j.ijmedinf.2022.104762.

2021

  • An empirical analysis of dealing with patients who are lost to follow-up when developing prognostic models using a cohort design. Reps JM, Rijnbeek PR, Cuthbert A, Ryan PB, Pratt N, Schuemie M BMC Med Inform Decis Mak. 2021. doi.org/10.1186/s12911-021-01408-x.
  • Design matters in patient-level prediction: evaluation of a cohort vs case-control design when developing predictive models in observational healthcare datasets. Reps JM, Ryan PB, Rijnbeek PR. J Big Data. 2021. doi.org/10.1186/s40537-021-00501-2.
  • Evaluating the impact of covariate lookback times on performance of patient-level prediction models. Hardin J, Reps JM. BMC Med Res Methodol. 2021. doi.org/10.1186/s12874-021-01370-2.
  • Investigating the impact of development and internal validation design when training prognostic models using a retrospective cohort in big US observational healthcare data. Reps JM, Ryan PB, Rijnbeek PR. BMJ Open. 2021. doi.org/10.1136/bmjopen-2021-050146.

2018

  • Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek PR. J Am Med Inform Assoc. 2018. doi.org/10.1093/jamia/ocy032.