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Peter Rijnbeek, PhD

Professor Medical Informatics

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Background

Peter Rijnbeek is Professor of Medical Informatics and is the Chair of the Department of Medical Informatics of the Erasmus MC, The Netherlands. He obtained his MSc (1996) in Electrical Engineering at the Technical University Delft. His PhD thesis, received from the Erasmus University Rotterdam, was on the development of a computer program to automatically interpret pediatric electrocardiograms.  His PhD topic was on computerized interpretation of the paediatric electrocardiogram (ECG). This work included research in machine learning algorithms for exhaustive search that learn optimal decision rules to support the cardiologist. He is the lead of the Health Data Science (HDS) research line at the Department and has a leading role in the Observational Health Data Sciences and Informatics (OHDSI) initiative. OHDSI is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics led by Columbia University. The data is stored in a common data model (CDM) to allow uniform and transparent analysis. Prof. Rijnbeek is leading the European OHDSI community that is working on the standardization of European databases to the CDM. His team addresses issues that deal with the quality of data, the translation of data to other formats, and the mechanisms to access data in a federated fashion. He is  the coordinator of the large European Health Data and Evidence Network (EHDEN) IMI project that builds a federated network of health data bases across Europe. Once stored in the CDM, the data can be used to build and validate predictive models, perform large-scale characterisation, and population-level effect estimation studies. The HDS Group is performing methods research in all these domains and is applying these approaches in network studies.  For example, for the research in the field of patient-level prediction, HDS is leading a patient-level prediction working group that includes data scientists from leading universities in the field and co-developed a framework for patient-level prediction models. Furthermore, Prof Rijnbeek is the executive director of the DARWIN EU(r) Coordination Center for the EMA that will develop and manage a network of real-world healthcare data sources across the EU and to conduct scientific studies requested by medicines regulators and, at a later stage, requested by other stakeholders. 

Publications

  • Yang C, Fridgeirsson EA, Kors JA, Reps JM, Rijnbeek PR, Wong J, et al. Does Using a Stacking Ensemble Method to Combine Multiple Base Learners Within a Database Improve Model Transportability? Stud Health Technol Inform. 2023;302:129-30.https://www.ncbi.nlm.nih.gov/pubmed/37203625
  • Williams RD, den Otter S, Reps JM, Rijnbeek PR. The DELPHI Library: Improving Model Validation, Transparency and Dissemination Through a Centralised Library of Prediction Models. Stud Health Technol Inform. 2023;302:139-40.https://www.ncbi.nlm.nih.gov/pubmed/37203630
  • Voss EA, Shoaibi A, Yin Hui Lai L, Blacketer C, Alshammari T, Makadia R, et al. Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study. EClinicalMedicine. 2023;58:101932.https://www.ncbi.nlm.nih.gov/pubmed/37034358
  • Voss EA, Blacketer C, van Sandijk S, Moinat M, Kallfelz M, van Speybroeck M, et al. European Health Data & Evidence Network-learnings from building out a standardized international health data network. J Am Med Inform Assoc. 2023.https://www.ncbi.nlm.nih.gov/pubmed/37952118
  • van Dijk ML, Te Loo LM, Vrijsen J, van den Akker-Scheek I, Westerveld S, Annema M, et al. LOFIT (Lifestyle front Office For Integrating lifestyle medicine in the Treatment of patients): a novel care model towards community-based options for lifestyle change-study protocol. Trials. 2023;24(1):114.https://www.ncbi.nlm.nih.gov/pubmed/36803271
  • Seinen TM, Kors JA, van Mulligen EM, Fridgeirsson E, Rijnbeek PR. The added value of text from Dutch general practitioner notes in predictive modeling. J Am Med Inform Assoc. 2023.https://www.ncbi.nlm.nih.gov/pubmed/37587084
  • Rekkas A, van Klaveren D, Ryan PB, Steyerberg EW, Kent DM, Rijnbeek PR. A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases. NPJ Digit Med. 2023;6(1):58.https://www.ncbi.nlm.nih.gov/pubmed/36991144
  • Rekkas A, Rijnbeek PR, Kent DM, Steyerberg EW, van Klaveren D. Estimating individualized treatment effects from randomized controlled trials: a simulation study to compare risk-based approaches. BMC Med Res Methodol. 2023;23(1):74.https://www.ncbi.nlm.nih.gov/pubmed/36977990
  • Raventos B, Fernandez-Bertolin S, Aragon M, Voss EA, Blacketer C, Mendez-Boo L, et al. Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research. Clin Epidemiol. 2023;15:969-86.https://www.ncbi.nlm.nih.gov/pubmed/37724311
  • Raventos B, Catala M, Du M, Guo Y, Black A, Inberg G, et al. IncidencePrevalence: An R package to calculate population-level incidence rates and prevalence using the OMOP common data model. Pharmacoepidemiol Drug Saf. 2023.https://www.ncbi.nlm.nih.gov/pubmed/37876360
  • Markus AF, Strauss VY, Burn E, Li X, Delmestri A, Reich C, et al. Characterising the treatment of thromboembolic events after COVID-19 vaccination in 4 European countries and the US: An international network cohort study. Front Pharmacol. 2023;14:1118203.https://www.ncbi.nlm.nih.gov/pubmed/37033631
  • Markus AF, Fridgeirsson EA, Kors JA, Verhamme KMC, Rijnbeek PR. Challenges of Estimating Global Feature Importance in Real-World Health Care Data. Stud Health Technol Inform. 2023;302:1057-61.https://www.ncbi.nlm.nih.gov/pubmed/37203580
  • Ly NF, Flach C, Lysen TS, Markov E, van Ballegooijen H, Rijnbeek P, et al. Impact of European Union Label Changes for Fluoroquinolone-Containing Medicinal Products for Systemic and Inhalation Use: Post-Referral Prescribing Trends. Drug Saf. 2023;46(4):405-16.https://www.ncbi.nlm.nih.gov/pubmed/36976448
  • Gauffin O, Brand JS, Vidlin SH, Sartori D, Asikainen S, Catala M, et al. Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study. Drug Saf. 2023.https://www.ncbi.nlm.nih.gov/pubmed/37804398
  • Arinze JT, de Ridder MAJ, Vojinovic D, van Ballegooijen H, Markov E, Duarte-Salles T, et al. Drug Utilisation Patterns of Alternatives to Ranitidine-Containing Medicines in Patients Treated with Ranitidine: A Network Analysis of Data from Six European National Databases. Drug Saf. 2023.https://www.ncbi.nlm.nih.gov/pubmed/37907775
  • Yang, C., et al., Development and external validation of prediction models for adverse health outcomes in rheumatoid arthritis: A multinational real-world cohort analysis. Seminars in Arthritis and Rheumatism, 2022. 56.
  • Yang, C., et al., Trends in the conduct and reporting of clinical prediction model development and validation: A systematic review. Journal of the American Medical Informatics Association, 2022. 29(5): p. 983-989.
  •  Wong, J., et al., Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations. Drug Safety, 2022. 45(5): p. 493-510.
  • Williams, R.D., et al., 90-Day all-cause mortality can be predicted following a total knee replacement: an international, network study to develop and validate a prediction model. Knee Surgery, Sports Traumatology, Arthroscopy, 2022. 30(9): p. 3068-3075.
  • Williams, R.D., et al., Using Iterative Pairwise External Validation to Contextualize Prediction Model Performance: A Use Case Predicting 1-Year Heart Failure Risk in Patients with Diabetes Across Five Data Sources. Drug Safety, 2022. 45(5): p. 563-570.
  • Williams, R.D., et al., Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network. BMC Medical Research Methodology, 2022. 22(1).
  • Voss, E.A., et al., Hip Fracture Risk After Treatment with Tramadol or Codeine: An Observational Study. Drug Safety, 2022. 45(7): p. 791-807.
  • Seinen, T.M., et al., Use of unstructured text in prognostic clinical prediction models: A systematic review. Journal of the American Medical Informatics Association, 2022. 29(7): p. 1292-1302.
  • Reps, J.M., et al., Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability. BMC Medical Informatics and Decision Making, 2022. 22(1).
  • Rekkas, A., et al., Individualized treatment effect was predicted best by modeling baseline risk in interaction with treatment assignment. arXiv preprint arXiv:2205.01717, 2022.
  • Ostropolets, A., et al., Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases. Frontiers in Pharmacology, 2022. 13.
  • Markus, A.F., et al., TreatmentPatterns: An R package to facilitate the standardized development and analysis of treatment patterns across disease domains. Computer Methods and Programs in Biomedicine, 2022. 225.
  • Markus, A.F., P.R. Rijnbeek, and J.M. Reps, Why predicting risk can’t identify ‘risk factors’: empirical assessment of model stability in machine learning across observational health databases. 2022.
  • Lin, V., et al., Training prediction models for individual risk assessment of postoperative complications after surgery for colorectal cancer. Techniques in Coloproctology, 2022. 26(8): p. 665-675.
  • Künnapuu, K., et al., Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. JAMIA open, 2022. 5(1): p. ooac021.
  • Kostka, K., et al., Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS. Clinical Epidemiology, 2022. 14: p. 369-384.
  • John, L.H., et al., Logistic regression models for patient-level prediction based on massive observational data: Do we need all data? International Journal of Medical Informatics, 2022. 163.
  • John, L.H., et al., External validation of existing dementia prediction models on observational health data. 2022.
  • de Ridder, M.A.J., et al., Data resource profile: the Integrated Primary Care Information (IPCI) database, the Netherlands. International Journal of Epidemiology, 2022.
  • Burn, E., et al., Background rates of five thrombosis with thrombocytopenia syndromes of special interest for COVID-19 vaccine safety surveillance: Incidence between 2017 and 2019 and patient profiles from 38.6 million people in six European countries. Pharmacoepidemiology and Drug Safety, 2022. 31(5): p. 495-510.
  • Burn, E., et al., Venous or arterial thrombosis and deaths among COVID-19 cases: a European network cohort study. The Lancet Infectious Diseases, 2022. 22(8): p. 1142-1152.
  • Baan, E.J., et al., Characterization of Asthma by Age of Onset: A Multi-Database Cohort Study. Journal of Allergy and Clinical Immunology: In Practice, 2022. 10(7): p. 1825-1834.e8.
  • Vogelsang, R.P., et al., Prediction of 90-day mortality after surgery for colorectal cancer using standardized nationwide quality-assurance data. BJS Open, 2021. 5(3).
  • Tan, E.H., et al., COVID-19 in patients with autoimmune diseases: Characteristics and outcomes in a multinational network of cohorts across three countries. Rheumatology (Bulgaria), 2021. 60(SI): p. SI37-SI50.
  • Reyes, C., et al., Characteristics and outcomes of patients with COVID-19 with and without prevalent hypertension: A multinational cohort study. BMJ Open, 2021. 11(12).
  • Reps, J.M., et al., 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 Data, 2021. 8(1).
  • Reps, J.M., P. Ryan, and P.R. Rijnbeek, Investigating the impact of development and internal validation design when training prognostic models using a retrospective cohort in big US observational healthcare data. BMJ Open, 2021. 11(12).
  • Reps, J.M., et al., 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 Making, 2021. 21(1).
  • Reps, J.M., et al., Implementation of the COVID-19 vulnerability index across an international network of health care data sets: Collaborative external validation study. JMIR Medical Informatics, 2021. 9(4).
  • Recalde, M., et al., Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom. International Journal of Obesity, 2021. 45(11): p. 2347-2357.
  • Prats-Uribe, A., et al., Use of repurposed and adjuvant drugs in hospital patients with covid-19: Multinational network cohort study. The BMJ, 2021. 373.
  • Morales, D.R., et al., Renin–angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis. The Lancet Digital Health, 2021. 3(2): p. e98-e114.
  • Markus, A.F., J.A. Kors, and P.R. Rijnbeek, The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics, 2021. 113.
  • Li, X., et al., Characterising the background incidence rates of adverse events of special interest for covid-19 vaccines in eight countries: Multinational network cohort study. The BMJ, 2021. 373.
  • Lane, J.C.E., et al., Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis: A multinational network cohort study. Rheumatology (United Kingdom), 2021. 60(7): p. 3222-3234.
  • Khalid, S., et al., A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data. Computer Methods and Programs in Biomedicine, 2021. 211.
  • Kent, S., et al., Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment. PharmacoEconomics, 2021. 39(3): p. 275-285.
  • James, G., et al., Treatment pathway analysis of newly diagnosed dementia patients in four electronic health record databases in Europe. Social Psychiatry and Psychiatric Epidemiology, 2021. 56(3): p. 409-416.
  • Duarte-Salles, T., et al., Thirty-day outcomes of children and adolescents with COVID-19: An international experience. Pediatrics, 2021. 148(3).
  • Blacketer, C., et al., Using the data quality dashboard to improve the ehden network. Applied Sciences (Switzerland), 2021. 11(24).
  • Blacketer, C., et al., Increasing trust in real-world evidence through evaluation of observational data quality. Journal of the American Medical Informatics Association : JAMIA, 2021. 28(10): p. 2251-2257.
  • Wang, Q., et al., Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network. PLoS ONE, 2020. 15(1).
  • Reps, J.M., et al., 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 Methodology, 2020. 20(1).
  • Rekkas, A., et al., Predictive approaches to heterogeneous treatment effects: a scoping review. BMC Medical Research Methodology, 2020. 20(1).
  • Perera, G., et al., Vascular and metabolic risk factor differences prior to dementia diagnosis: A multidatabase case-control study using European electronic health records. BMJ Open, 2020. 10(11).
  • Newby, D., et al., Methotrexate and relative risk of dementia amongst patients with rheumatoid arthritis: A multi-national multi-database case-control study. Alzheimer's Research and Therapy, 2020. 12(1).
  • Lane, J.C.E., et al., Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study. The Lancet Rheumatology, 2020. 2(11): p. e698-e711.
  • Kim, Y., et al., Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis. Scientific Reports, 2020. 10(1).
  • Jin, S., et al., Prediction of major depressive disorder following beta-blocker therapy in patients with cardiovascular diseases. Journal of Personalized Medicine, 2020. 10(4): p. 1-12.
  • James, G., et al., Treatment pathway analysis of newly diagnosed dementia patients in four electronic health record databases in Europe. Social Psychiatry and Psychiatric Epidemiology, 2020.
  • Engelkes, M., et al., Multinational cohort study of mortality in patients with asthma and severe asthma.Respiratory Medicine, 2020. 165.
  • Engelkes, M., et al., Incidence, risk factors and re-exacerbation rate of severe asthma exacerbations in a multinational, multidatabase pediatric cohort study. Pediatric Allergy and Immunology, 2020. 31(5): p. 496-505.
  • Burn, E., et al., Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study. Nature Communications, 2020. 11(1).
  • Boland, M.R., et al., Female Reproductive Performance and Maternal Birth Month: A Comprehensive Meta-Analysis Exploring Multiple Seasonal Mechanisms. Scientific Reports, 2020. 10(1).
  • Berencsi, K., et al., Impact of risk minimisation measures on the use of strontium ranelate in Europe: a multi-national cohort study in 5 EU countries by the EU-ADR Alliance. Osteoporosis International, 2020. 31(4): p. 721-755.
  • Berencsi, K., et al., Correction to: Impact of risk minimisation measures on the use of strontium ranelate in Europe: a multi-national cohort study in 5 EU countries by the EU-ADR Alliance (Osteoporosis International, (2020), 31, 4, (721-755), 10.1007/s00198-019-05181-6). Osteoporosis International, 2020. 31(4): p. 799.
  • Ali, M.S., et al., Comparative cardiovascular safety of strontium ranelate and bisphosphonates: a multi-database study in 5 EU countries by the EU-ADR Alliance. Osteoporosis International, 2020. 31(12): p. 2425-2438.
  • van den Berg, M.E., et al., Corrigendum: Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages (Frontiers in Physiology, (2018), 9, (424), 10.3389/fphys.2018.00424). Frontiers in Physiology, 2019. 10.
  • van den Berg, M.E., et al., Normal Values of QT Variability in 10-s Electrocardiograms for all Ages.Frontiers in Physiology, 2019. 10.
  • Reps, J.M., P.R. Rijnbeek, and P.B. Ryan, Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data. Drug Safety, 2019.
  • Reps, J.M., P.R. Rijnbeek, and P.B. Ryan, Supplementing claims data analysis using self-reported data to develop a probabilistic phenotype model for current smoking status. Journal of Biomedical Informatics, 2019. 97.
  • Almeida, J.R., et al., TASKA: A modular task management system to support health research studies.BMC Medical Informatics and Decision Making, 2019. 19(1).
  • Alexander, M., et al., Risks and clinical predictors of cirrhosis and hepatocellular carcinoma diagnoses in adults with diagnosed NAFLD: Real-world study of 18 million patients in four European cohorts. BMC Medicine, 2019. 17(1).
  • Alexander, M., et al., Non-alcoholic fatty liver disease and risk of incident acute myocardial infarction and stroke: Findings from matched cohort study of 18 million European adults. The BMJ, 2019. 367.
  • van den Berg, M.E., et al., Normal values of corrected heart-rate variability in 10-second electrocardiograms for all ages. Frontiers in Physiology, 2018. 9(APR).
  • Reps, J.M., et al., 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, 2018. 25(8): p. 969-975.
  • Perera, G., et al., Dementia prevalence and incidence in a federation of European Electronic Health Record databases: The European Medical Informatics Framework resource. Alzheimer's and Dementia, 2018. 14(2): p. 130-139.
  • Pan, X., et al., Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genomics, 2018. 19(1).
  • Osokogu, O.U., et al., Impact of different assumptions on estimates of childhood diseases obtained from health care data: A retrospective cohort study. Pharmacoepidemiology and Drug Safety, 2018. 27(6): p. 612-620.
  • Fajarda, O., et al. A methodology to perform semi-automatic distributed EHR database queries. in 11th International Conference on Health Informatics, HEALTHINF 2018 - Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018. 2018.
  • Alexander, M., et al., Real-world data reveal a diagnostic gap in non-alcoholic fatty liver disease. BMC Medicine, 2018. 16(1).
  • Voss, E.A., et al., Accuracy of an automated knowledge base for identifying drug adverse reactions.Journal of Biomedical Informatics, 2017. 66: p. 72-81.
  • Trifirò, G., et al., Use of azithromycin and risk of ventricular arrhythmia. CMAJ, 2017. 189(15): p. E560-E568.
  • Rijnbeek, P.R., et al., Validation of automatic measurement of QT interval variability. PLoS ONE, 2017. 12(4).
  • Perera, G., et al., Dementia prevalence and incidence in a federation of European Electronic Health Record databases: The European Medical Informatics Framework resource. Alzheimer's and Dementia, 2017.
  • Roberto, G., et al., Identifying cases of type 2 diabetes in heterogeneous data sources: Strategy from the EMIF project. PLoS ONE, 2016. 11(8).
  • Noordam, R., et al., Antidepressants and heart-rate variability in older adults: A population-based study.Psychological Medicine, 2016. 46(6): p. 1239-1247.
  • Niemeijer, M.N., et al., Subclinical Abnormalities in Echocardiographic Parameters and Risk of Sudden Cardiac Death in a General Population: The Rotterdam Study. Journal of Cardiac Failure, 2016. 22(1): p. 17-23.
  • Mor, A., et al., Erratum to: Antibiotic use varies substantially among adults: a cross-national study from five European Countries in the ARITMO project (Infection, 43, 453-472,(2015) DOI 10.1007/s15010-015-0768-8). Infection, 2016. 44(1): p. 145-150.
  • Coloma, P.M., et al., Risk of cardiac valvulopathy with use of bisphosphonates: a population-based, multi-country case-control study. Osteoporosis International, 2016. 27(5): p. 1857-1867.
  • Chaker, L., et al., Thyroid Function and Sudden Cardiac Death: A Prospective Population-Based Cohort Study. Circulation, 2016. 134(10): p. 713-722.
  • Bezemer, I.D., et al., Use of oral contraceptives in three European countries: A population-based multi-database study. European Journal of Contraception and Reproductive Health Care, 2016. 21(1): p. 81-87.
  • Noordam, R., et al., Assessing prolongation of the heart rate corrected QT interval in users of tricyclic antidepressants. Journal of Clinical Psychopharmacology, 2015. 35(3): p. 260-265.
  • Noordam, R., et al., Antidepressants and heart-rate variability in older adults: a population-based study.Psychol Med, 2015: p. 1-9.
  • Niemeijer, M.N., et al., Declining incidence of sudden cardiac death from 1990-2010 in a general middle-aged and elderly population: The Rotterdam Study. Heart Rhythm, 2015. 12(1): p. 123-129.
  • Niemeijer, M.N., et al., Drugs and ventricular repolarization in a general population: the Rotterdam Study. Pharmacoepidemiol Drug Saf, 2015. 24(10): p. 1036-41.
  • Niemeijer, M.N., et al., Pharmacogenetics of Drug-Induced QT Interval Prolongation: An Update. Drug Saf, 2015. 38(10): p. 855-67.
  • Niemeijer, M.N., et al., Consistency of heart rate-QTc prolongation consistency and sudden cardiac death: The Rotterdam Study. Heart Rhythm, 2015. 12(10): p. 2078-85.
  • Niemeijer, M.N., et al., ABCB1 gene variants, digoxin and risk of sudden cardiac death in a general population. Heart, 2015. 101(24): p. 1973-1979.
  • Niemeijer, M.N., et al., ABCB1 gene variants, digoxin and risk of sudden cardiac death in a general population. Heart, 2015. 101(24): p. 1973-9.
  • Niemeijer, M.N., et al., Subclinical Abnormalities in Echocardiographic Parameters and Risk of Sudden Cardiac Death in a General Population: The Rotterdam Study. Journal of Cardiac Failure, 2015.
  • Mor, A., et al., Erratum to: Antibiotic use varies substantially among adults: a cross-national study from five European Countries in the ARITMO project. Infection, 2015.
  • Mor, A., et al., Antibiotic use varies substantially among adults: a cross-national study from five European Countries in the ARITMO project. Infection, 2015. 43(4): p. 453-472.
  • Lahousse, L., et al., Chronic obstructive pulmonary disease and sudden cardiac death: the Rotterdam study. Eur Heart J, 2015. 36(27): p. 1754-61.
  • Italiano, D., et al., Effectiveness of risk minimization measures for cabergoline-induced cardiac valve fibrosis in clinical practice in Italy. Journal of Neural Transmission, 2015. 122(6): p. 799-808.
  • Hripcsak, G., et al., Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers. 15th World Congress on Health and Biomedical Informatics, MEDINFO 2015, 2015. 216: p. 574-578.
  • Coloma, P.M., et al., Risk of cardiac valvulopathy with use of bisphosphonates: a population-based, multi-country case-control study. Osteoporosis International, 2015: p. 1-11.
  • Trifirò, G., et al., Combining multiple healthcare databases for postmarketing drug and vaccine safety surveillance: Why and how? Journal of Internal Medicine, 2014. 275(6): p. 551-561.
  • Rijnbeek, P.R., et al., Normal values of the electrocardiogram for ages 16-90 years. Journal of Electrocardiology, 2014. 47(6): p. 914-921.
  • Rijnbeek, P.R., Erratum to Converting to a Common Data Model: What is Lost in Translation? [Drug Safety, DOI 10.1007/s40264-014-0221-4]. Drug Safety, 2014. 37(12): p. 1073.
  • Rijnbeek, P.R., Converting to a Common Data Model: What is Lost in Translation?: Commentary on “Fidelity Assessment of a Clinical Practice Research Datalink Conversion to the OMOP Common Data Model”. Drug Safety, 2014. 37(11): p. 893-896.
  • Niemeijer, M.N., et al., Short-term QT variability markers for the prediction of ventricular arrhythmias and sudden cardiac death: A systematic review. Heart, 2014. 100(23): p. 1831-1836.
  • Metz, C., et al., Alignment of 4D coronary CTA with monoplane X-ray angiography. 6th International Workshop on Augmented Environments for Computer-Assisted Interventions, AE-CAI 2011, Held in Conjunction with the Medical Image Computing and Computer-Assisted Interventions, MICCAI 2011, 2012. 7264 LNCS: p. 106-116.
  • Van Der Putten, N.H.J.J., et al. Validation of electrocardiographic criteria for predicting the culprit artery in patients with acute myocardial infarction. in Computing in Cardiology 2010, CinC 2010. 2010. Belfast.
  • Rijnbeek, P.R. and J.A. Kors, Finding a short and accurate decision rule in disjunctive normal form by exhaustive search. Machine Learning, 2010. 80(1): p. 33-62.
  • Rijnbeek, P.R., et al., Electrocardiographic criteria for left ventricular hypertrophy in children. Pediatric Cardiology, 2008. 29(5): p. 923-928.
  • Kors, J.A., et al., Spatial repolarization parameters for predicting cardiac death in the elderly. Journal of Electrocardiology, 2004. 37(SUPPL.): p. 198-200.
  • Wu, J., et al., Normal limits of the electrocardiogram in Chinese subjects. International Journal of Cardiology, 2003. 87(1): p. 37-51.
  • Verbraak, A.F.M., et al., A new approach to mechanical simulation of lung behaviour: Pressure-controlled and time-related piston movement. Medical and Biological Engineering and Computing, 2001. 39(1): p. 82-89.
  • Rijnbeek, P.R., et al., Pedmeans: A computer program for the interpretation of pediatric electrocardiograms. Journal of Electrocardiology, 2001. 34(SUPPL.): p. 85-91.
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