| Can machine-learning improve cardiovascular risk prediction using routine clinical data? SF Weng, J Reps, J Kai, JM Garibaldi, N Qureshi PloS one 12 (4), e0174944, 2017 | 1406 | 2017 |
| Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data JM Reps, MJ Schuemie, MA Suchard, PB Ryan, PR Rijnbeek Journal of the American Medical Informatics Association 25 (8), 969-975, 2018 | 201 | 2018 |
| Safety of hydroxychloroquine, alone and in combination with azithromycin, in light of rapid wide-spread use for COVID-19: a multinational, network cohort and self-controlled … JCE Lane, J Weaver, K Kostka, T Duarte-Salles, MTF Abrahao, H Alghoul, ... MedRxiv, 2020 | 175 | 2020 |
| Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study JCE Lane, J Weaver, K Kostka, T Duarte-Salles, MTF Abrahao, H Alghoul, ... The Lancet Rheumatology 2 (11), e698-e711, 2020 | 132 | 2020 |
| Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer GP Figueredo, PO Siebers, MR Owen, J Reps, U Aickelin PloS one 9 (4), e95150, 2014 | 83 | 2014 |
| Finding factors that predict treatment‐resistant depression: Results of a cohort study MS Cepeda, J Reps, P Ryan Depression and anxiety 35 (7), 668-673, 2018 | 78 | 2018 |
| Perinatal depressive symptoms often start in the prenatal rather than postpartum period: results from a longitudinal study M Wilcox, BA McGee, DF Ionescu, M Leonte, L LaCross, J Reps, ... Archives of women's mental health 24 (1), 119-131, 2021 | 68 | 2021 |
| Using machine learning applied to real-world healthcare data for predictive analytics: an applied example in bariatric surgery SS Johnston, JM Morton, I Kalsekar, EM Ammann, CW Hsiao, J Reps Value in health 22 (5), 580-586, 2019 | 55 | 2019 |
| Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network RD Williams, AF Markus, C Yang, T Duarte-Salles, SL DuVall, T Falconer, ... BMC Medical Research Methodology 22 (1), 35, 2022 | 51 | 2022 |
| Finding treatment‐resistant depression in real‐world data: How a data‐driven approach compares with expert‐based heuristics MS Cepeda, J Reps, D Fife, C Blacketer, P Stang, P Ryan Depression and anxiety 35 (3), 220-228, 2018 | 51 | 2018 |
| Comparison of algorithms that detect drug side effects using electronic healthcare databases JM Reps, JM Garibaldi, U Aickelin, D Soria, J Gibson, R Hubbard Soft Computing 17, 2381-2397, 2013 | 38* | 2013 |
| Illness beliefs predict mortality in patients with diabetic foot ulcers K Vedhara, K Dawe, JNV Miles, MA Wetherell, N Cullum, C Dayan, ... PloS one 11 (4), e0153315, 2016 | 37 | 2016 |
| Feasibility and evaluation of a large-scale external validation approach for patient-level prediction in an international data network: validation of models predicting stroke … JM Reps, RD Williams, SC You, T Falconer, E Minty, A Callahan, PB Ryan, ... BMC medical research methodology 20, 1-10, 2020 | 34 | 2020 |
| Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network Q Wang, JM Reps, KF Kostka, PB Ryan, Y Zou, EA Voss, PR Rijnbeek, ... PLoS One 15 (1), e0226718, 2020 | 33 | 2020 |
| Machine-learning model to predict the cause of death using a stacking ensemble method for observational data C Kim, SC You, JM Reps, JY Cheong, RW Park Journal of the American Medical Informatics Association 28 (6), 1098-1107, 2021 | 32 | 2021 |
| Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study A Nestsiarovich, JM Reps, ME Matheny, SL DuVall, KE Lynch, M Beaton, ... Translational psychiatry 11 (1), 642, 2021 | 30 | 2021 |
| A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations JM Reps, JM Garibaldi, U Aickelin, JE Gibson, RB Hubbard Journal of biomedical informatics 56, 356-368, 2015 | 27 | 2015 |
| DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models C Luo, MN Islam, NE Sheils, J Buresh, J Reps, MJ Schuemie, PB Ryan, ... Nature communications 13 (1), 1678, 2022 | 26 | 2022 |
| Quiet in class: classification, noise and the dendritic cell algorithm F Gu, J Feyereisl, R Oates, J Reps, J Greensmith, U Aickelin Artificial Immune Systems: 10th International Conference, ICARIS 2011 …, 2011 | 26 | 2011 |
| Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases U Chandran, J Reps, PE Stang, PB Ryan PLoS One 14 (12), e0226255, 2019 | 24 | 2019 |
