OHDSI Community Publications

最新更新时间 2019年1月

2018

 

Albers, D. J., Elhadad, N., Claassen, J., Perotte, R., Goldstein, A., & Hripcsak, G. (2018). Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms. Journal of biomedical informatics, 78, 87–101. https://doi.org/10.1016/j.jbi.2018.01.004
 Blaisure, J. C., & Ceusters, W. M. (2018). Improving the ‘Fitness for Purpose’ of Common Data Models through Realism Based Ontology. AMIA Annual Symposium Proceedings, 2017, 440–447.
 Boland, M. R., Parhi, P., Li, L., Miotto, R., Carroll, R., Iqbal, U., … Tatonetti, N. P. (2018). Uncovering exposures responsible for birth season – disease effects: a global study. Journal of the American Medical Informatics Association, 25(3), 275–288. https://doi.org/10.1093/jamia/ocx105
 Butler, A., Wei, W., Yuan, C., Kang, T., Si, Y., & Weng, C. (2018). The Data Gap in the EHR for Clinical Research Eligibility Screening. AMIA Summits on Translational Science Proceedings, 2017, 320–329.
 Maier, C., Lang, L., Storf, H., Vormstein, P., Bieber, R., Bernarding, J., … Sedlmayr, M. (2018). Towards Implementation of OMOP in a German University Hospital Consortium. Applied Clinical Informatics, 9(1), 54–61. https://doi.org/10.1055/s-0037-1617452
 Natsiavas, P., Boyce, R. D., Jaulent, M.-C., & Koutkias, V. (2018). OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies. Frontiers in Pharmacology, 9. https://doi.org/10.3389/fphar.2018.00609
 Nestsiarovich, A., Mazurie, A. J., Hurwitz, N. G., Kerner, B., Nelson, S. J., Crisanti, A. S., … Lambert, C. G. (2018). Comprehensive comparison of monotherapies for psychiatric hospitalization risk in bipolar disorders. Bipolar Disorders, 20(8), 761–771. https://doi.org/10.1111/bdi.12665
 Pacaci, A., Gonul, S., Sinaci, A. A., Yuksel, M., & Laleci Erturkmen, G. B. (2018). A Semantic Transformation Methodology for the Secondary Use of Observational Healthcare Data in Postmarketing Safety Studies. Frontiers in Pharmacology, 9. https://doi.org/10.3389/fphar.2018.00435
 Patadia, V. K., Schuemie, M. J., Coloma, P. M., Herings, R., van der Lei, J., Sturkenboom, M., & Trifirò, G. (2018). Can Electronic Health Records Databases Complement Spontaneous Reporting System Databases? A Historical-Reconstruction of the Association of Rofecoxib and Acute Myocardial Infarction. Frontiers in Pharmacology, 9, 594. https://doi.org/10.3389/fphar.2018.00594
 Polubriaginof, F. C. G., Vanguri, R., Quinnies, K., Belbin, G. M., Yahi, A., Salmasian, H., … Tatonetti, N. P. (2018). Disease Heritability Inferred from Familial Relationships Reported in Medical Records. Cell, 173(7), 1692-1704.e11. https://doi.org/10.1016/j.cell.2018.04.032
 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: JAMIA, 25(8), 969–975. https://doi.org/10.1093/jamia/ocy032
 Ryan, P. B., Buse, J. B., Schuemie, M. J., DeFalco, F., Yuan, Z., Stang, P. E., … Rosenthal, N. (2018). Comparative effectiveness of canagliflozin, SGLT2 inhibitors and non-SGLT2 inhibitors on the risk of hospitalization for heart failure and amputation in patients with type 2 diabetes mellitus: A real-world meta-analysis of 4 observational databases (OBSERVE-4D). Diabetes, Obesity & Metabolism, 20(11), 2585–2597. https://doi.org/10.1111/dom.13424
 Schuemie, M. J., Hripcsak, G., Ryan, P. B., Madigan, D., & Suchard, M. A. (2018). Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data. Proceedings of the National Academy of Sciences of the United States of America, 115(11), 2571–2577. https://doi.org/10.1073/pnas.1708282114
 Tian, Y., Schuemie, M. J., & Suchard, M. A. (2018). Evaluating large-scale propensity score performance through real-world and synthetic data experiments. International Journal of Epidemiology, 47(6), 2005–2014. https://doi.org/10.1093/ije/dyy120
 Vilar, S., Friedman, C., & Hripcsak, G. (2018). Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media. Briefings in Bioinformatics, 19(5), 863–877. https://doi.org/10.1093/bib/bbx010
 Yang, Y., Zhou, X., Gao, S., Lin, H., Xie, Y., Feng, Y., … Zhan, S. (2018). Evaluation of Electronic Healthcare Databases for Post-Marketing Drug Safety Surveillance and Pharmacoepidemiology in China. Drug Safety, 41(1), 125–137. https://doi.org/10.1007/s40264-017-0589-z
 

2017

 

Banda, J. M., Halpern, Y., Sontag, D., & Shah, N. H. (2017). Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network. AMIA Summits on Translational Science Proceedings, 2017, 48–57.
 Boland, M. R., Karczewski, K. J., & Tatonetti, N. P. (2017). Ten Simple Rules to Enable Multi-site Collaborations through Data Sharing. PLoS Computational Biology, 13(1). https://doi.org/10.1371/journal.pcbi.1005278
 Boland, M. R., Polubriaginof, F., & Tatonetti, N. P. (2017). Development of A Machine Learning Algorithm to Classify Drugs Of Unknown Fetal Effect. Scientific Reports, 7. https://doi.org/10.1038/s41598-017-12943-x
 Callahan, T. J., Bauck, A. E., Bertoch, D., Brown, J., Khare, R., Ryan, P. B., … Kahn, M. G. (2017). A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks. eGEMs, 5(1). https://doi.org/10.5334/egems.223
 Ceusters, W., & Blaisure, J. (2017). A Realism-Based View on Counts in OMOP’s Common Data Model. Studies in Health Technology and Informatics, 237, 55–62.
 Chakrabarti, S., Sen, A., Huser, V., Hruby, G. W., Rusanov, A., Albers, D. J., & Weng, C. (2017). An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model version 5.0. Journal of Healthcare Informatics Research, 1(1), 1–18. https://doi.org/10.1007/s41666-017-0005-6
 Chiu, P.-H., & Hripcsak, G. (2017). EHR-Based Phenotyping: Bulk Learning and Evaluation. Journal of biomedical informatics, 70, 35–51. https://doi.org/10.1016/j.jbi.2017.04.009
 Duke, J. D., Ryan, P. B., Suchard, M. A., Hripcsak, G., Jin, P., Reich, C., … Schuemie, M. J. (2017). Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network. Epilepsia, 58(8), e101–e106. https://doi.org/10.1111/epi.13828
 Gini, R., Schuemie, M. J., Pasqua, A., Carlini, E., Profili, F., Cricelli, I., … Klazinga, N. (2017). Monitoring compliance with standards of care for chronic diseases using healthcare administrative databases in Italy: Strengths and limitations. PLoS ONE, 12(12). https://doi.org/10.1371/journal.pone.0188377
 Harpaz, R., DuMouchel, W., Schuemie, M., Bodenreider, O., Friedman, C., Horvitz, E., … Shah, N. H. (2017). Toward multimodal signal detection of adverse drug reactions. Journal of Biomedical Informatics, 76, 41–49. https://doi.org/10.1016/j.jbi.2017.10.013
 Huser, V., Kahn, M. G., Brown, J. S., & Gouripeddi, R. (2017). Methods for examining data quality in healthcare integrated data repositories. 收入 Biocomputing 2018 (卷 1–0, 页 628–633). WORLD SCIENTIFIC. https://doi.org/10.1142/9789813235533_0059
 Jiang, G., Kiefer, R. C., Sharma, D. K., Prud’hommeaux, E., & Solbrig, H. R. (2017). A Consensus-based Approach for Harmonizing the OHDSI Common Data Model with HL7 FHIR. Studies in health technology and informatics, 245, 887–891.
 Jiang, G., Kiefer, R., Prud’hommeaux, E., & Solbrig, H. R. (2017). Building Interoperable FHIR-based Vocabulary Mapping Services: A Case Study of OHDSI Vocabularies and Mappings. Studies in health technology and informatics, 245, 1327.
 Kang, T., Zhang, S., Tang, Y., Hruby, G. W., Rusanov, A., Elhadad, N., & Weng, C. (2017). EliIE: An open-source information extraction system for clinical trial eligibility criteria. Journal of the American Medical Informatics Association : JAMIA, 24(6), 1062–1071. https://doi.org/10.1093/jamia/ocx019
 Kim, H., Choi, J., Jang, I., Quach, J., & Ohno-Machado, L. (2017). Feasibility of Representing Data from Published Nursing Research Using the OMOP Common Data Model. AMIA Annual Symposium Proceedings, 2016, 715–723.
 Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data. (2017). Journal of Biomedical Semantics, 8. https://doi.org/10.1186/s13326-017-0115-3
 Lee, S., Choi, J., Kim, H.-S., Kim, G. J., Lee, K. H., Park, C. H., … Kim, J. H. (2017). Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records. Journal of the American Medical Informatics Association, 24(4), 697–708. https://doi.org/10.1093/jamia/ocw168
 Moskovitch, R., Polubriaginof, F., Weiss, A., Ryan, P., & Tatonetti, N. (2017). Procedure prediction from symbolic Electronic Health Records via time intervals analytics. Journal of Biomedical Informatics, 75, 70–82. https://doi.org/10.1016/j.jbi.2017.07.018
 Natarajan, S., Bangera, V., Khot, T., Picado, J., Wazalwar, A., Costa, V. S., … Caldwell, M. (2017). Markov Logic Networks for Adverse Drug Event Extraction from Text. Knowledge and information systems, 51(2), 435–457. https://doi.org/10.1007/s10115-016-0980-6
 Nissim, N., Shahar, Y., Boland, M. R., Tatonetti, N. P., Elovici, Y., Hripcsak, G., & Moskovitch, R. (2017). Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods. Artificial intelligence in medicine, 81, 12–32. https://doi.org/10.1016/j.artmed.2017.03.003
 Ong, T. C., Kahn, M. G., Kwan, B. M., Yamashita, T., Brandt, E., Hosokawa, P., … Schilling, L. M. (2017). Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading. BMC Medical Informatics and Decision Making, 17. https://doi.org/10.1186/s12911-017-0532-3
 Park, R. W. (2017). Sharing Clinical Big Data While Protecting Confidentiality and Security: Observational Health Data Sciences and Informatics. Healthcare Informatics Research, 23(1), 1–3. https://doi.org/10.4258/hir.2017.23.1.1
 Rosenbloom, S. T., Carroll, R. J., Warner, J. L., Matheny, M. E., & Denny, J. C. (2017). Representing Knowledge Consistently Across Health Systems. Yearbook of Medical Informatics, 26(1), 139–147. https://doi.org/10.15265/IY-2017-018
 Sen, A., Ryan, P. B., Goldstein, A., Chakrabarti, S., Wang, S., Koski, E., & Weng, C. (2017). Correlating eligibility criteria generalizability and adverse events using Big Data for patients and clinical trials. Annals of the New York Academy of Sciences, 1387(1), 34–43. https://doi.org/10.1111/nyas.13195
 Shang, N., Weng, C., & Hripcsak, G. (2017). A conceptual framework for evaluating data suitability for observational studies. Journal of the American Medical Informatics Association: JAMIA. https://doi.org/10.1093/jamia/ocx095
 Si, Y., & Weng, C. (2017). An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria. Studies in health technology and informatics, 245, 950–954.
 Voss, E. A., Boyce, R. D., Ryan, P. B., van der Lei, J., Rijnbeek, P. R., & Schuemie, M. J. (2017). Accuracy of an automated knowledge base for identifying drug adverse reactions. Journal of Biomedical Informatics, 66, 72–81. https://doi.org/10.1016/j.jbi.2016.12.005
 You, S. C., Lee, S., Cho, S.-Y., Park, H., Jung, S., Cho, J., … Park, R. W. (2017). Conversion of National Health Insurance Service-National Sample Cohort (NHIS-NSC) Database into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). Studies in Health Technology and Informatics, 245, 467–470.

2016

 

Banda, J. M., Halpern, Y., Sontag, D., & Shah, N. H. (2017). Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network. AMIA Summits on Translational Science Proceedings, 2017, 48–57.
 Boland, M. R., Karczewski, K. J., & Tatonetti, N. P. (2017). Ten Simple Rules to Enable Multi-site Collaborations through Data Sharing. PLoS Computational Biology, 13(1). https://doi.org/10.1371/journal.pcbi.1005278
 Boland, M. R., Polubriaginof, F., & Tatonetti, N. P. (2017). Development of A Machine Learning Algorithm to Classify Drugs Of Unknown Fetal Effect. Scientific Reports, 7. https://doi.org/10.1038/s41598-017-12943-x
 Callahan, T. J., Bauck, A. E., Bertoch, D., Brown, J., Khare, R., Ryan, P. B., … Kahn, M. G. (2017). A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks. eGEMs, 5(1). https://doi.org/10.5334/egems.223
 Ceusters, W., & Blaisure, J. (2017). A Realism-Based View on Counts in OMOP’s Common Data Model. Studies in Health Technology and Informatics, 237, 55–62.
 Chakrabarti, S., Sen, A., Huser, V., Hruby, G. W., Rusanov, A., Albers, D. J., & Weng, C. (2017). An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model version 5.0. Journal of Healthcare Informatics Research, 1(1), 1–18. https://doi.org/10.1007/s41666-017-0005-6
 Chiu, P.-H., & Hripcsak, G. (2017). EHR-Based Phenotyping: Bulk Learning and Evaluation. Journal of biomedical informatics, 70, 35–51. https://doi.org/10.1016/j.jbi.2017.04.009
 Duke, J. D., Ryan, P. B., Suchard, M. A., Hripcsak, G., Jin, P., Reich, C., … Schuemie, M. J. (2017). Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network. Epilepsia, 58(8), e101–e106. https://doi.org/10.1111/epi.13828
 Gini, R., Schuemie, M. J., Pasqua, A., Carlini, E., Profili, F., Cricelli, I., … Klazinga, N. (2017). Monitoring compliance with standards of care for chronic diseases using healthcare administrative databases in Italy: Strengths and limitations. PLoS ONE, 12(12). https://doi.org/10.1371/journal.pone.0188377
 Harpaz, R., DuMouchel, W., Schuemie, M., Bodenreider, O., Friedman, C., Horvitz, E., … Shah, N. H. (2017). Toward multimodal signal detection of adverse drug reactions. Journal of Biomedical Informatics, 76, 41–49. https://doi.org/10.1016/j.jbi.2017.10.013
 Huser, V., Kahn, M. G., Brown, J. S., & Gouripeddi, R. (2017). Methods for examining data quality in healthcare integrated data repositories. 收入 Biocomputing 2018 (卷 1–0, 页 628–633). WORLD SCIENTIFIC. https://doi.org/10.1142/9789813235533_0059
 Jiang, G., Kiefer, R. C., Sharma, D. K., Prud’hommeaux, E., & Solbrig, H. R. (2017). A Consensus-based Approach for Harmonizing the OHDSI Common Data Model with HL7 FHIR. Studies in health technology and informatics, 245, 887–891.
 Jiang, G., Kiefer, R., Prud’hommeaux, E., & Solbrig, H. R. (2017). Building Interoperable FHIR-based Vocabulary Mapping Services: A Case Study of OHDSI Vocabularies and Mappings. Studies in health technology and informatics, 245, 1327.
 Kang, T., Zhang, S., Tang, Y., Hruby, G. W., Rusanov, A., Elhadad, N., & Weng, C. (2017). EliIE: An open-source information extraction system for clinical trial eligibility criteria. Journal of the American Medical Informatics Association : JAMIA, 24(6), 1062–1071. https://doi.org/10.1093/jamia/ocx019
 Kim, H., Choi, J., Jang, I., Quach, J., & Ohno-Machado, L. (2017). Feasibility of Representing Data from Published Nursing Research Using the OMOP Common Data Model. AMIA Annual Symposium Proceedings, 2016, 715–723.
 Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data. (2017). Journal of Biomedical Semantics, 8. https://doi.org/10.1186/s13326-017-0115-3
 Lee, S., Choi, J., Kim, H.-S., Kim, G. J., Lee, K. H., Park, C. H., … Kim, J. H. (2017). Standard-based comprehensive detection of adverse drug reaction signals from nursing statements and laboratory results in electronic health records. Journal of the American Medical Informatics Association, 24(4), 697–708. https://doi.org/10.1093/jamia/ocw168
 Moskovitch, R., Polubriaginof, F., Weiss, A., Ryan, P., & Tatonetti, N. (2017). Procedure prediction from symbolic Electronic Health Records via time intervals analytics. Journal of Biomedical Informatics, 75, 70–82. https://doi.org/10.1016/j.jbi.2017.07.018
 Natarajan, S., Bangera, V., Khot, T., Picado, J., Wazalwar, A., Costa, V. S., … Caldwell, M. (2017). Markov Logic Networks for Adverse Drug Event Extraction from Text. Knowledge and information systems, 51(2), 435–457. https://doi.org/10.1007/s10115-016-0980-6
 Nissim, N., Shahar, Y., Boland, M. R., Tatonetti, N. P., Elovici, Y., Hripcsak, G., & Moskovitch, R. (2017). Inter-Labeler and Intra-Labeler Variability of Condition Severity Classification Models Using Active and Passive Learning Methods. Artificial intelligence in medicine, 81, 12–32. https://doi.org/10.1016/j.artmed.2017.03.003
 Ong, T. C., Kahn, M. G., Kwan, B. M., Yamashita, T., Brandt, E., Hosokawa, P., … Schilling, L. M. (2017). Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading. BMC Medical Informatics and Decision Making, 17. https://doi.org/10.1186/s12911-017-0532-3
 Park, R. W. (2017). Sharing Clinical Big Data While Protecting Confidentiality and Security: Observational Health Data Sciences and Informatics. Healthcare Informatics Research, 23(1), 1–3. https://doi.org/10.4258/hir.2017.23.1.1
 Rosenbloom, S. T., Carroll, R. J., Warner, J. L., Matheny, M. E., & Denny, J. C. (2017). Representing Knowledge Consistently Across Health Systems. Yearbook of Medical Informatics, 26(1), 139–147. https://doi.org/10.15265/IY-2017-018
 Sen, A., Ryan, P. B., Goldstein, A., Chakrabarti, S., Wang, S., Koski, E., & Weng, C. (2017). Correlating eligibility criteria generalizability and adverse events using Big Data for patients and clinical trials. Annals of the New York Academy of Sciences, 1387(1), 34–43. https://doi.org/10.1111/nyas.13195
 Shang, N., Weng, C., & Hripcsak, G. (2017). A conceptual framework for evaluating data suitability for observational studies. Journal of the American Medical Informatics Association: JAMIA. https://doi.org/10.1093/jamia/ocx095
 Si, Y., & Weng, C. (2017). An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria. Studies in health technology and informatics, 245, 950–954.
 Voss, E. A., Boyce, R. D., Ryan, P. B., van der Lei, J., Rijnbeek, P. R., & Schuemie, M. J. (2017). Accuracy of an automated knowledge base for identifying drug adverse reactions. Journal of Biomedical Informatics, 66, 72–81. https://doi.org/10.1016/j.jbi.2016.12.005
 You, S. C., Lee, S., Cho, S.-Y., Park, H., Jung, S., Cho, J., … Park, R. W. (2017). Conversion of National Health Insurance Service-National Sample Cohort (NHIS-NSC) Database into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). Studies in Health Technology and Informatics, 245, 467–470.

2015

 

Ayvaz, S., Horn, J., Hassanzadeh, O., Zhu, Q., Stan, J., Tatonetti, N. P., … Boyce, R. D. (2015). Toward a complete dataset of drug-drug interaction information from publicly available sources. Journal of biomedical informatics, 55, 206–217. https://doi.org/10.1016/j.jbi.2015.04.006
 Boland, M. R., Shahn, Z., Madigan, D., Hripcsak, G., & Tatonetti, N. P. (2015). Birth month affects lifetime disease risk: a phenome-wide method. Journal of the American Medical Informatics Association : JAMIA, 22(5), 1042–1053. https://doi.org/10.1093/jamia/ocv046
 Boland, M. R., & Tatonetti, N. P. (2015). Are All Vaccines Created Equal? Using Electronic Health Records to Discover Vaccines Associated With Clinician-Coded Adverse Events. AMIA Summits on Translational Science Proceedings, 2015, 196–200.
 Boland, M. R., Tatonetti, N. P., & Hripcsak, G. (2015). Development and validation of a classification approach for extracting severity automatically from electronic health records. Journal of Biomedical Semantics, 6. https://doi.org/10.1186/s13326-015-0010-8
 de Bie, S., Coloma, P. M., Ferrajolo, C., Verhamme, K. M. C., Trifirò, G., Schuemie, M. J., … Sturkenboom, M. C. J. M. (2015). The role of electronic healthcare record databases in paediatric drug safety surveillance: a retrospective cohort study. British Journal of Clinical Pharmacology, 80(2), 304–314. https://doi.org/10.1111/bcp.12610
 FitzHenry, F., Resnic, F. S., Robbins, S. L., Denton, J., Nookala, L., Meeker, D., … Matheny, M. E. (2015). Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership. Applied Clinical Informatics, 6(3), 536–547. https://doi.org/10.4338/ACI-2014-12-CR-0121
 Hripcsak, G., Duke, J. D., Shah, N. H., Reich, C. G., Huser, V., Schuemie, M. J., … Ryan, P. B. (2015). Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers. Studies in health technology and informatics, 216, 574–578.
 Kahn, M. G., Brown, J. S., Chun, A. T., Davidson, B. N., Meeker, D., Ryan, P. B., … Zozus, M. N. (2015). Transparent Reporting of Data Quality in Distributed Data Networks. eGEMs, 3(1). https://doi.org/10.13063/2327-9214.1052
 Li, Y., Ryan, P. B., Wei, Y., & Friedman, C. (2015). A Method to Combine Signals from Spontaneous Reporting Systems and Observational Healthcare Data to Detect Adverse Drug Reactions. Drug Safety, 38(10), 895–908. https://doi.org/10.1007/s40264-015-0314-8
 Overhage, J. M., Ryan, P. B., Schuemie, M. J., & Stang, P. E. (2015). Authors’ Reply to Hennessy and Leonard’s Comment on “Desideratum for Evidence-Based Epidemiology”. Drug Safety, 38(1), 105–107. https://doi.org/10.1007/s40264-014-0254-8
 Panaccio, M. P., Cummins, G., Wentworth, C., Lanes, S., Reynolds, S. L., Reynolds, M. W., … Koren, A. (2015). A common data model to assess cardiovascular hospitalization and mortality in atrial fibrillation patients using administrative claims and medical records. Clinical Epidemiology, 7, 77–90. https://doi.org/10.2147/CLEP.S64936
 Patadia, V. K., Coloma, P., Schuemie, M. J., Herings, R., Gini, R., Mazzaglia, G., … Trifirò, G. (2015). Using real-world healthcare data for pharmacovigilance signal detection – the experience of the EU-ADR project. Expert Review of Clinical Pharmacology, 8(1), 95–102. https://doi.org/10.1586/17512433.2015.992878
 Vilar, S., Lorberbaum, T., Hripcsak, G., & Tatonetti, N. P. (2015). Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling. PloS One, 10(6), e0129974. https://doi.org/10.1371/journal.pone.0129974
 Voss, E. A., Ma, Q., & Ryan, P. B. (2015). The impact of standardizing the definition of visits on the consistency of multi-database observational health research. BMC Medical Research Methodology, 15, 13. https://doi.org/10.1186/s12874-015-0001-6
 Voss, E. A., Makadia, R., Matcho, A., Ma, Q., Knoll, C., Schuemie, M., … Ryan, P. B. (2015). Feasibility and utility of applications of the common data model to multiple, disparate observational health databases. Journal of the American Medical Informatics Association: JAMIA, 22(3), 553–564. https://doi.org/10.1093/jamia/ocu023
 Voss, E. A., Ryan, P. B., Stang, P. E., Hough, D., & Alphs, L. (2015). Switching from risperidone long-acting injectable to paliperidone long-acting injectable or oral antipsychotics: analysis of a Medicaid claims database. International Clinical Psychopharmacology, 30(3), 151–157. https://doi.org/10.1097/YIC.0000000000000068
 Xu, Y., Zhou, X., Suehs, B. T., Hartzema, A. G., Kahn, M. G., Moride, Y., … Bate, A. (2015). A Comparative Assessment of Observational Medical Outcomes Partnership and Mini-Sentinel Common Data Models and Analytics: Implications for Active Drug Safety Surveillance. Drug Safety: An International Journal of Medical Toxicology and Drug Experience; Auckland, 38(8), 749–765.

2012 - 2014

 

Ayvaz, S., Zhu, Q., Hochheiser, H., Brochhausen, M., Horn, J., Dumontier, M., … Boyce, R. D. (2014). Drug-Drug Interaction Data Source Survey and Linking. AMIA Summits on Translational Science Proceedings, 2014, 16.
 Bell, C., Chakravarty, A., Gruber, S., Heckbert, S. R., Levenson, M., Martin, D., … Walker, A. M. (2014). Characteristics of study design and elements that may contribute to the success of electronic safety monitoring systems. Pharmacoepidemiology and Drug Safety, 23(11), 1223–1225. https://doi.org/10.1002/pds.3712
 DeFalco, F. J., Ryan, P. B., & Soledad Cepeda, M. (2013). Applying standardized drug terminologies to observational healthcare databases: a case study on opioid exposure. Health Services & Outcomes Research Methodology, 13(1), 58–67. https://doi.org/10.1007/s10742-012-0102-1
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