OHDSI KOREA


OHDSI 컨소시엄에 소속된 스탠포드대학교, 콜롬비아대학교, 홍콩대학교, UCLA, 존슨앤존슨 등과 함께 범세계적으로 다기관의 임상데이터를 활용하여 연구를 활발히 수행하고 있습니다.

  • Clinical Concept Value Sets and Interoperability in Health Data Analytics.
    AMIA Annu Symp Proc. 2018 Dec 5;2018:480-489. eCollection 2018.
    Gold S, Batch A, McClure R, Jiang G, Kharrazi H, Saripalle R, Huser V, Weng C, Roderer N, Szarfman A, Elmqvist N, Gotz D.
    https://www.ncbi.nlm.nih.gov/pubmed/30815088

  • HemOnc: A New Standard Vocabulary for Chemotherapy Regimen Representation in the OMOP Common Data Model.
    J Biomed Inform. 2019 Jun 22:103239. doi: 10.1016/j.jbi.2019.103239.
    Warner JL, Dymshyts D, Reich CG, Gurley MJ, Hochheiser H, Moldwin ZH, Belenkaya R, Williams AE, Yang PC.
    https://www.ncbi.nlm.nih.gov/pubmed/?term=HemOnc%3A+A+new+standard+vocabulary+for+chemotherapy+regimen+representation+in+the+OMOP+common+data+model

  • Identifying the DEAD: Development and Validation of a Patient-Level Model to Predict Death Status in Population-Level Claims Data.
    Drug Saf. 2019 May 3. doi: 10.1007/s40264-019-00827-0.
    Reps JM, Rijnbeek PR, Ryan PB.
    https://www.ncbi.nlm.nih.gov/pubmed/?term=Identifying+the+DEAD%3A+Development+and+Validation+of+a+Patient-Level+Model+to+Predict+Death+Status+in+Population-Level+Claims+Data

  • Facilitating phenotype transfer using a common data model.
    J Biomed Inform. 2019 Jul 17:103253. doi: 10.1016/j.jbi.2019.103253.
    Hripcsak G, Shang N, Peissig PL, Rasmussen LV, Liu C, Benoit B, Carroll RJ, Carrell DS, Denny JC, Dikilitas O, Gainer VS, Marie Howell K, Klann JG, Kullo IJ, Lingren T, Mentch FD, Murphy SN, Natarajan K, Pacheco JA, Wei WQ, Wiley K, Weng C.
    https://www.ncbi.nlm.nih.gov/pubmed/31325501

  • Genomic Common Data Model for Seamless Interoperation of Biomedical Data in Clinical Practice: Retrospective Study.
    J Med Internet Res. 2019 Mar 26;21(3):e13249. doi: 10.2196/13249.
    Shin SJ, You SC, Park YR, Roh J, Kim JH, Haam S, Reich CG, Blacketer C, Son DS, Oh S, Park RW.
    https://www.ncbi.nlm.nih.gov/pubmed/30912749

  • ADEpedia-on-OHDSI: A next Generation Pharmacovigilance Signal Detection Platform Using the OHDSI Common Data Model.
    Journal of Biomedical Informatics 91 (March 1, 2019): 103119.
    Yu, Yue, Kathryn J. Ruddy, Na Hong, Shintaro Tsuji, Andrew Wen, Nilay D. Shah, and Guoqian Jiang.
    https://doi.org/10.1016/j.jbi.2019.103119

  • Effect of Vocabulary Mapping for Conditions on Phenotype Cohorts.
    Journal of the American Medical Informatics Association, 2018.
    Hripcsak, George, Matthew E. Levine, Ning Shang, and Patrick B. Ryan.
    https://doi.org/10.1093/jamia/ocy124

  • Application and optimisation of the Comparison on Extreme Laboratory Tests (CERT) algorithm for detection of adverse drug reactions: Transferability across national boundaries.
    Pharmacoepidemiol Drug Saf. 2018 Jan;27(1):87-94. doi: 10.1002/pds.4340. Epub 2017 Nov 6.
    Tham MY, Ye Q, Ang PS, Fan LY, Yoon D, Park RW, Ling ZJ, Yip JW, Tai BC, Evans SJ, Sung C.
    https://www.ncbi.nlm.nih.gov/pubmed/29108136

  • Web Services for Data Warehouses: OMOP and PCORnet on I2b2.
    Journal of the American Medical Informatics Association 25, no. 10 (October 1, 2018): 1331–38.
    Klann, Jeffrey G., Lori C. Phillips, Christopher Herrick, Matthew A. H. Joss, Kavishwar B. Wagholikar, and Shawn N. Murphy.
    https://doi.org/10.1093/jamia/ocy093

  • Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin: Analysis From the Observational Health Data.
    JAMA Netw Open. 2018 Aug 3;1(4):e181755. doi: 10.1001/jamanetworkopen.2018.1755.
    Vashisht R, Jung K, Schuler A, Banda JM, Park RW, Jin S, Li L, Dudley JT, Johnson KW, Shervey MM, Xu H, Wu Y, Natrajan K, Hripcsak G, Jin P, Van Zandt M, Reckard A, Reich CG, Weaver J, Schuemie MJ, Ryan PB, Callahan A, Shah NH.
    https://www.ncbi.nlm.nih.gov/pubmed/30646124

  • Applying a common data model to Asian databases for multinational pharmacoepidemiologic studies: opportunities and challenges.
    Clin Epidemiol. 2018 Jul 27;10:875-885. doi: 10.2147/CLEP.S149961. eCollection 2018.
    Lai EC, Ryan P, Zhang Y, Schuemie M, Hardy NC, Kamijima Y, Kimura S, Kubota K, Man KK, Cho SY, Park RW, Stang P, Su CC, Wong IC, Kao YY, Setoguchi S.
    https://www.ncbi.nlm.nih.gov/pubmed/3010076

  • 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 and Metabolism 0, no. 0 (n.d.). 25 June 2018.
    Ryan, Patrick B., John B. Buse, Martijn J. Schuemie, Frank DeFalco, Zhong Yuan, Paul E. Stang, Jesse A. Berlin, and Norman Rosenthal.
    https://doi.org/10.1111/dom.13424

  • 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, April 27, 2018.
    Reps, Jenna M., Martijn J. Schuemie, Marc A. Suchard, Patrick B. Ryan, and Peter R. Rijnbeek.
    https://doi.org/10.1093/jamia/ocy032.

  • Risk of Lower Extremity Amputations in People with Type 2 Diabetes Mellitus Treated with Sodium‐glucose Co‐transporter‐2 Inhibitors in the USA: A Retrospective Cohort Study.
    Diabetes, Obesity & Metabolism 20, no. 3 (March 2018): 582–89.
    Yuan, Zhong, Frank J. DeFalco, Patrick B. Ryan, Martijn J. Schuemie, Paul E. Stang, Jesse A. Berlin, Mehul Desai, and Norm Rosenthal.
    https://doi.org/10.1111/dom.13115

  • Conversion of National Health Insurance Service-National Sample Cohort (NHIS-NSC) Database into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM).
    Stud Health Technol Inform. 2017;245:467-470.
    You SC, Lee S, Cho SY, Park H, Jung S, Cho J, Yoon D, Park RW.
    https://www.ncbi.nlm.nih.gov/pubmed/29295138

  • Uncovering exposures responsible for birth season - disease effects: a global study.
    J Am Med Inform Assoc. 2017 Sep 28. doi: 10.1093/jamia/ocx105. [Epub ahead of print]
    Boland MR, Parhi P, Li L, Miotto R, Carroll R, Iqbal U, Nguyen PA, Schuemie M, You SC, Smith D, Mooney S, Ryan P, Li YJ, Park RW, Denny J, Dudley JT, Hripcsak G, Gentine P, Tatonetti NP.
    https://www.ncbi.nlm.nih.gov/pubmed/29036387

  • Risk Prediction for Ischemic Stroke and Transient Ischemic Attack in Patients Without Atrial Fibrillation: A Retrospective Cohort Study.
    Journal of Stroke and Cerebrovascular Diseases 26, no. 8 (August 1, 2017): 1721–31.
    Yuan, Zhong, Erica A. Voss, Frank J. DeFalco, Guohua Pan, Patrick B. Ryan, Daniel Yannicelli, and Christopher Nessel.
    https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.03.036.

  • Risk of Angioedema Associated with Levetiracetam Compared with Phenytoin: Findings of the Observational Health Data Sciences and Informatics Research Network.
    Epilepsia, First published: 06 July 2017
    Duke, Jon D., Patrick B. Ryan, Marc A. Suchard, George Hripcsak, Peng Jin, Christian Reich, Marie-Sophie Schwalm, et al.
    https://doi.org/10.1111/epi.13828

  • Accuracy of an Automated Knowledge Base for Identifying Drug Adverse Reactions.
    Journal of Biomedical Informatics 66 (February 1, 2017): 72–81.
    Voss, E. A., R. D. Boyce, P. B. Ryan, J. van der Lei, P. R. Rijnbeek, and M. J. Schuemie.
    https://doi.org/10.1016/j.jbi.2016.12.005

  • Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets.
    EGEMS (Wash DC). 2016 Nov 30;4(1):1239. doi: 10.13063/2327-9214.1239. eCollection 2016.
    Huser V, DeFalco FJ, Schuemie M, Ryan PB, Shang N, Velez M, Park RW, Boyce RD, Duke J, Khare R, Utidjian L, Bailey C.
    https://www.ncbi.nlm.nih.gov/pubmed/28154833

  • Characterizing treatment pathways at scale using the OHDSI network.
    Proc Natl Acad Sci U S A. 2016 Jul 5;113(27):7329-36. doi: 10.1073/pnas.1510502113. Epub 2016 Jun 6.
    Hripcsak G, Ryan PB, Duke JD, Shah NH, Park RW, Huser V, Suchard MA, Schuemie MJ, DeFalco FJ, Perotte A, Banda JM, Reich CG, Schilling LM, Matheny ME, Meeker D, Pratt N, Madigan D.
    https://www.ncbi.nlm.nih.gov/pubmed/27274072

  • A Curated and Standardized Adverse Drug Event Resource to Accelerate Drug Safety Research.
    Scientific Data 3 (May 10, 2016): 160026.
    Banda, Juan M., Lee Evans, Rami S. Vanguri, Nicholas P. Tatonetti, Patrick B. Ryan, and Nigam H. Shah.
    https://doi.org/10.1038/sdata.2016.26

  • Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research.
    Healthc Inform Res. 2016 Jan;22(1):54-8. doi: 10.4258/hir.2016.22.1.54. Epub 2016 Jan 31.
    Yoon D, Ahn EK, Park MY, Cho SY, Ryan P, Schuemie MJ, Shin D, Park H, Park RW.
    https://www.ncbi.nlm.nih.gov/pubmed/26893951

  • Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers.
    Stud Health Technol Inform. 2015;216:574-8.
    Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, Suchard MA, Park RW, Wong IC, Rijnbeek PR, van der Lei J, Pratt N, Norén GN, Li YC, Stang PE, Madigan D, Ryan PB.
    https://www.ncbi.nlm.nih.gov/pubmed/26262116

  • Fidelity Assessment of a Clinical Practice Research Datalink Conversion to the OMOP Common Data Model.
    Drug Safety 37, no. 11 (2014): 945–59.
    Matcho, Amy, Patrick Ryan, Daniel Fife, and Christian Reich.
    https://doi.org/10.1007/s40264-014-0214-3.

  • Validation of a Common Data Model for Active Safety Surveillance Research.
    Journal of the American Medical Informatics Association : JAMIA 19, no. 1 (2012): 54–60.
    Overhage, J Marc, Patrick B Ryan, Christian G Reich, Abraham G Hartzema, and Paul E Stang.
    https://doi.org/10.1136/amiajnl-2011-000376.