Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm. - Feng Gu 006; Jan Feyereisl; Robert F. Oates; Jenna Reps; Julie Greensmith; Uwe Aickelin (ICARIS, 2011)

Discovering sequential patterns in a UK general practice database. - Jenna Reps; Jonathan M. Garibaldi; Uwe Aickelin; Daniele Soria; Jack E. Gibson; Richard B. Hubbard (BHI, 2012)

Comparing data-mining algorithms developed for longitudinal observational databases. - Jenna Reps; Jonathan M. Garibaldi; Uwe Aickelin; Daniele Soria; Jack E. Gibson; Richard B. Hubbard (UKCI, 2012)

Attributes for causal inference in electronic healthcare databases. - Jenna Reps; Jonathan M. Garibaldi; Uwe Aickelin; Daniele Soria; Jack E. Gibson; Richard B. Hubbard (CBMS, 2013)

Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database. - Jenna Reps; Jan Feyereisl; Jonathan M. Garibaldi; Uwe Aickelin; Jack E. Gibson; Richard B. Hubbard (CoRR, 2013)

Comparison of algorithms that detect drug side effects using electronic healthcare databases. - Jenna Marie Reps; Jonathan M. Garibaldi; Uwe Aickelin; Daniele Soria; Jack E. Gibson; Richard B. Hubbard (Soft Comput., 2013)

Tuning a multiple classifier system for side effect discovery using genetic algorithms. - Jenna Marie Reps; Uwe Aickelin; Jonathan M. Garibaldi (IEEE Congress on Evolutionary Computation, 2014)

Investigating distance metric learning in semi-supervised fuzzy c-means clustering. - Daphne Teck Ching Lai; Jonathan M. Garibaldi; Jenna Reps (FUZZ-IEEE, 2014)

Incorporating Spontaneous Reporting System Data to Aid Causal Inference in Longitudinal Healthcare Data. - Jenna Marie Reps; Uwe Aickelin (ICDM Workshops, 2014)

Personalising Mobile Advertising Based on Users' Installed Apps. - Jenna Reps; Uwe Aickelin; Jonathan M. Garibaldi; Chris Damski (ICDM Workshops, 2014)

Refining Adverse Drug Reactions Using Association Rule Mining for Electronic Healthcare Data. - Jenna Marie Reps; Uwe Aickelin; Jiangang Ma; Yanchun Zhang (ICDM Workshops, 2014)

Comparing Stochastic Differential Equations and Agent-Based Modelling and Simulation for Early-stage Cancer. - Grazziela P. Figueredo; Peer-Olaf Siebers; Markus R. Owen; Jenna Reps; Uwe Aickelin (CoRR, 2014)

A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery. - Jenna Reps; Jonathan M. Garibaldi; Uwe Aickelin; Daniele Soria; Jack E. Gibson; Richard B. Hubbard (CoRR, 2014)

Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. - Jenna Reps; Jonathan M. Garibaldi; Uwe Aickelin; Daniele Soria; Jack E. Gibson; Richard B. Hubbard (CoRR, 2014)

Attributes for Causal Inference in Longitudinal Observational Databases. - Jenna Reps; Jonathan M. Garibaldi; Uwe Aickelin; Daniele Soria; Jack E. Gibson; Richard B. Hubbard (CoRR, 2014)

A Novel Semisupervised Algorithm for Rare Prescription Side Effect Discovery. - Jenna Marie Reps; Jonathan M. Garibaldi; Uwe Aickelin; Daniele Soria; Jack E. Gibson; Richard B. Hubbard (IEEE J. Biomed. Health Informatics, 2014)

Detecting adverse drug reactions in the general practice healthcare database. - Jenna Marie Reps (2014)

Identifying Candidate Risk Factors for Prescription Drug Side Effects Using Causal Contrast Set Mining. - Jenna Reps; Zhaoyang Guo; Haoyue Zhu; Uwe Aickelin (HIS, 2015)

A supervised adverse drug reaction signalling framework imitating Bradford Hill's causality considerations. - Jenna Marie Reps; Jonathan M. Garibaldi; Uwe Aickelin; Jack E. Gibson; Richard B. Hubbard (J. Biomed. Informatics, 2015)

Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining. - Jenna Marie Reps; Uwe Aickelin; Richard B. Hubbard (Comput. Biol. Medicine, 2016)

Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data. - Jenna Marie Reps; Martijn J. Schuemie; Marc A. Suchard; Patrick B. Ryan; Peter R. Rijnbeek (J. Am. Medical Informatics Assoc., 2018)

Using simulation to incorporate dynamic criteria into multiple criteria decision-making. - Uwe Aickelin; Jenna Marie Reps; Peer-Olaf Siebers; Peng Li (J. Oper. Res. Soc., 2018)

Learning Across a Healthcare Data Network to Improve Model Robustness and Evidence Reliability. - Noémie Elhadad; Iñigo Urteaga; Alison Callahan; Jenna Reps; Patrick B. Ryan (AMIA, 2019)

Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking status. - Jenna Marie Reps; Peter R. Rijnbeek; Patrick B. Ryan (J. Biomed. Informatics, 2019)

How little data do we need for patient-level prediction? - Luis H. John; Jan A. Kors; Jenna Marie Reps; Patrick B. Ryan; Peter R. Rijnbeek (CoRR, 2020)

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data. - Sara 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; et al. (Comput. Methods Programs Biomed., 2021)

Machine-learning model to predict the cause of death using a stacking ensemble method for observational data. - Chungsoo Kim; Seng Chan You; Jenna Marie Reps; Jae Youn Cheong; Rae Woong Park (J. Am. Medical Informatics Assoc., 2021)

Design matters in patient-level prediction: evaluation of a cohort vs. case-control design when developing predictive models in observational healthcare datasets. - Jenna Marie Reps; Patrick B. Ryan; Peter R. Rijnbeek; Martijn J. Schuemie (J. Big Data, 2021)

An empirical analysis of dealing with patients who are lost to follow-up when developing prognostic models using a cohort design. - Jenna Marie Reps; Peter R. Rijnbeek; Alana Cuthbert; Patrick B. Ryan; Nicole Pratt; Martijn J. Schuemie (BMC Medical Informatics Decis. Mak., 2021)

Why predicting risk can't identify 'risk factors': empirical assessment of model stability in machine learning across observational health databases. - Aniek F. Markus; Peter R. Rijnbeek; Jenna Marie Reps (MLHC, 2022)

Logistic regression models for patient-level prediction based on massive observational data: Do we need all data? - Luis H. John; Jan A. Kors; Jenna Marie Reps; Patrick B. Ryan; Peter R. Rijnbeek (Int. J. Medical Informatics, 2022)

Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary. - Stephen P. Fortin; Jenna Reps; Patrick B. Ryan (BMC Medical Informatics Decis. Mak., 2022)

Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability. - Jenna Marie Reps; Ross D. Williams; Martijn J. Schuemie; Patrick B. Ryan; Peter R. Rijnbeek (BMC Medical Informatics Decis. Mak., 2022)

Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research. - Martijn J. Schuemie; Jenna Reps; Adam Black; Frank J. DeFalco; Lee Evans; Egill A. Fridgeirsson; James P. Gilbert; Chris Knoll; Martin Lavallee; Gowtham A. Rao; Peter R. Rijnbeek; Katy Sadowski; et al. (MedInfo, 2023)

The DELPHI Library: Improving Model Validation, Transparency and Dissemination Through a Centralised Library of Prediction Models. - Ross D. Williams; Sicco den Otter; Jenna Marie Reps; Peter R. Rijnbeek (MIE, 2023)

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)

Correction to: Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary. - Stephen P. Fortin; Jenna Reps; Patrick B. Ryan (BMC Medical Informatics Decis. Mak., 2023)

Comparison of deep learning and conventional methods for disease onset prediction. - Luis H. John; Chungsoo Kim; Jan A. Kors; Junhyuk Chang; Hannah Morgan-Cooper; Priya Desai; Chao Pang; Peter R. Rijnbeek; Jenna Marie Reps; Egill A. Fridgeirsson (CoRR, 2024)

Comparing penalization methods for linear models on large observational health data. - Egill A. Fridgeirsson; Ross D. Williams; Peter R. Rijnbeek; Marc A. Suchard; Jenna Marie Reps (J. Am. Medical Informatics Assoc., 2024)

Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data. - Cynthia Yang; Egill A. Fridgeirsson; Jan A. Kors; Jenna Marie Reps; Peter R. Rijnbeek (J. Big Data, 2024)

Extensive benchmarking of a method that estimates external model performance from limited statistical characteristics. - Tal El-Hay; Jenna Marie Reps; Chen Yanover (npj Digit. Medicine, 2025)

Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm. - Jiayi Tong; Jenna Marie Reps; Chongliang Luo; Yiwen Lu; Lu Li; Juan Manuel Ramirez-Anguita; Milou T. Brand; Scott L. DuVall; Thomas Falconer; Alex Mayer Fuentes; Xing He 003; Michael E. Matheny; et al. (npj Digit. Medicine, 2025)

COLA-GLM: collaborative one-shot and lossless algorithms of generalized linear models for decentralized observational healthcare data. - Qiong Wu; Jenna Marie Reps; Lu Li; Bingyu Zhang; Yiwen Lu; Jiayi Tong; Dazheng Zhang; Thomas Lumley; Milou T. Brand; Mui Van Zandt; Thomas Falconer; Xing He 003; et al. (npj Digit. Medicine, 2025)