Results 21 to 30 of about 1,444 (142)

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 studyResearch in context

EClinicalMedicine, 2023
Summary: Background: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19.
Erica A. Voss, Azza Shoaibi, Lana Yin Hui Lai, Clair Blacketer, Thamir Alshammari, Rupa Makadia, Kevin Haynes, Anthony G. Sena, Gowtham Rao, Sebastiaan van Sandijk, Clement Fraboulet, Laurent Boyer, Tanguy Le Carrour, Scott Horban, Daniel R. Morales, Jordi Martínez Roldán, Juan Manuel Ramírez-Anguita, Miguel A. Mayer, Marcel de Wilde, Luis H. John, Talita Duarte-Salles, Elena Roel, Andrea Pistillo, Raivo Kolde, Filip Maljković, Spiros Denaxas, Vaclav Papez, Michael G. Kahn, Karthik Natarajan, Christian Reich, Alex Secora, Evan P. Minty, Nigam H. Shah, Jose D. Posada, Maria Teresa Garcia Morales, Diego Bosca, Honorio Cadenas Juanino, Antonio Diaz Holgado, Miguel Pedrera Jiménez, Pablo Serrano Balazote, Noelia García Barrio, Selçuk Şen, Ali Yağız Üresin, Baris Erdogan, Luc Belmans, Geert Byttebier, Manu L.N.G. Malbrain, Daniel J. Dedman, Zara Cuccu, Rohit Vashisht, Atul J. Butte, Ayan Patel, Lisa Dahm, Cora Han, Fan Bu, Faaizah Arshad, Anna Ostropolets, Fredrik Nyberg, George Hripcsak, Marc A. Suchard, Dani Prieto-Alhambra, Peter R. Rijnbeek, Martijn J. Schuemie, Patrick B. Ryan   +63 more
doaj   +1 more source

Core Concepts in Pharmacoepidemiology: Multi-Database Distributed Data Networks. [PDF]

Pharmacoepidemiol Drug Saf
ABSTRACT Multi‐database distributed data networks for post‐marketing surveillance of drug safety and effectiveness use two main approaches: common data models (CDMs) and common protocols. Networks such as the U.S. Sentinel System, the Observational Health Data Sciences and Informatics (OHDSI) network, and the Data Analysis and Real‐World Interrogation ...
Haber R, Webster-Clark M, Pratt N, Barclay N, Li X, Maro JC, Platt RW, Prieto-Alhambra D, Filion KB.   +8 more
europepmc   +2 more sources

Aioli: Standardising Drugs in the FDA Adverse Event Reporting System (FAERS) to RxNorm and Anatomical Therapeutic Chemical (ATC) Codes. [PDF]

Pharmacoepidemiol Drug Saf
ABSTRACT Purpose The Food and Drug Administration Adverse Event Reporting System (FAERS) is an important source of information on suspected adverse drug reactions, but does not standardise drugs. The Adverse Event Open Learning Through Universal Standardization (AEOLUS) System Provides Standardisation of drugs in FAERS to RxNorm, but its coverage ...
Parry RE, Pera V, Verhamme KMC, de Wilde M, van Mulligen EM, Kors JA.   +5 more
europepmc   +2 more sources

Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study

Journal of Medical Internet Research, 2021
BackgroundElectronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data
Antoine Lamer, Osama Abou-Arab, Alexandre Bourgeois, Adrien Parrot, Benjamin Popoff, Jean-Baptiste Beuscart, Benoît Tavernier, Mouhamed Djahoum Moussa   +7 more
doaj   +1 more source

ADEpedia-on-OHDSI: A next generation pharmacovigilance signal detection platform using the OHDSI common data model [PDF]

Journal of Biomedical Informatics, 2019
Supplementing the Spontaneous Reporting System (SRS) with Electronic Health Record (EHR) data for adverse drug reaction detection could augment sample size, increase population heterogeneity and cross-validate results for pharmacovigilance research. The difference in the underlying data structures and terminologies between SRS and EHR data presents ...
Yue, Yu, Kathryn J, Ruddy, Na, Hong, Shintaro, Tsuji, Andrew, Wen, Nilay D, Shah, Guoqian, Jiang   +6 more
openaire   +2 more sources

Dermatologic Research Potential of the Observational Health Data Sciences and Informatics (OHDSI) Network [PDF]

Dermatology, 2021
<b><i>Background:</i></b> The Observational Health Data Sciences and Informatics (OHDSI) network enables access to billions of deidentified, standardized health records and built-in analytics software for observational health research, with numerous potential applications to dermatology.
Torunn Elise Sivesind, Taylor Runion, Megan Branda, Lisa M. Schilling, Robert P. Dellavalle   +4 more
openaire   +2 more sources

Glucagon-Like Peptide 1 Receptor Agonists and Chronic Lower Respiratory Disease Among Type 2 Diabetes Patients: Replication and Reliability Assessment Across a Research Network. [PDF]

Pharmacoepidemiol Drug Saf
ABSTRACT Introduction The aim of this study is to use observational methods to evaluate reliability of evidence generated by a study of the effect of glucagon‐like peptide 1 receptor agonists (GLP‐1RA) on chronic lower respiratory disease (CLRD) outcomes among Type‐2 diabetes mellitus (T2DM) patients.
Conover MM, Albogami Y, Hardin J, Reich CG, Ostropolets A, Ryan PB, Observational Health Data Sciences and Informatics (OHDSI) Research Network.   +6 more
europepmc   +2 more sources

Characterizing treatment pathways at scale using the OHDSI network [PDF]

Proceedings of the National Academy of Sciences, 2016
Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research ...
Hripcsak, G., Ryan, P.B., Duke, J.D., Shah, N.H., Park, R.W., Huser, V., Suchard, M.A., Schuemie, M.J., DeFalco, F.J., Perotte, A., Banda, J.M., Reich, C.G., Schilling, L.M., Matheny, M.E., Meeker, D., Pratt, N., Madigan, D.   +16 more
openaire   +5 more sources

OHDSI Germany – Join the Journey: A Workshop

, 2021
The Observational Health Data Science and Informatics (OHDSI) is an international collaboration that enables researchers to conduct observational studies around the globe based on standardized data and methods that was founded in 2014 [ref:1]. OHDSI provides a common data model (OMOP CDM) [for full text, please go to the a.m. URL]
Reinecke, I, Zoch, M, Reich, C, Kallfelz, M, Grewe, N, Sedlmayr, M   +5 more
openaire   +2 more sources

Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data. [PDF]

Proteomics
ABSTRACT The integration of big data into nephrology research will open new avenues for analyzing and understanding complex biological datasets, driving advances in personalized management of kidney diseases. This paper describes the multifaceted challenges and opportunities by incorporating big data in nephrology, emphasizing the importance of data ...
Stojanov R, Jovanovik M, Gramatikov S, Mishkovski I, Zdravevski E, Sasanski D, Karapancheva Z, Spasovski G, Vasileska I, Eftimov T, Zhuojun W, Jankowski J, Trajanov D.   +12 more
europepmc   +2 more sources