Advancing the Science for Active Surveillance: Rationale and Design for the Observational Medical Outcomes Partnership

By Paul E. Stang, P. Ryan, J. Racoosin, J. Marc Overhage, A. Hartzema, C. Reich, E. Welebob, Thomas Scarnecchia, J. Woodcock. 2010

432 citations recorded by the source corpus.

Subfields: Public Health, Environmental and Occupational Health, Statistics and Probability, Toxicology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Public Health, Environmental and Occupational Health

1. David Madigan
2. Benjamin Letham
3. Tyler H. McCormick
4. Cynthia Rudin

In Statistics and Probability

1. Cynthia Rudin
2. Nigam H. Shah
3. Robert Eriksson
4. Carol Friedman
5. David Madigan
6. William DuMouchel
7. Lars Juhl Jensen
8. Thomas Werge

In Toxicology

1. Santiago Vilar
2. Carol Friedman
3. Lars Juhl Jensen
4. Søren Brunak

Citations in the corpus

• Building Interpretable Classifiers with Rules using Bayesian Analysis (2012, background): “org) has mapped to a common data model [39].”
• An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis (2013, background): “This database forms part of the suite of databases that the Observational Medical Outcomes Partnership (OMOP, http://omop.fnih.org) has mapped to a common data model (Stang et al. 2010).”
• Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions (2013, methodology): “7 20 22 To date, electronic healthcare data have been used in pharmacovigilance predominantly to con fi rm signals originating from SRSs on an ad hoc basis.”
• Dose-Specific Adverse Drug Reaction Identification in Electronic Patient Records: Temporal Data Mining in an Inpatient Psychiatric Population (2014, background): “The FDA Sentinel Initiative [12], the EU-ADR project [16] and the Observational Medical Outcomes Partnership [17] aim at large-scale aggregation and integration of data captured in databases.”
• An interpretable model for stroke prediction using rules and Bayesian analysis (2014, methodology): “The BRL method introduced in this work aims to hit the “sweet spot” between predictive accuracy, interpretability, and tractability.”
• Validation of a common data model for active safety surveillance research (2012, methodology): “health-related conditions.(5) To facilitate this methodological research, OMOP is evaluating the performance of numerous analytic methods for identifying drugeoutcome associations across multiple disparate observational data sources using a CDM(6) and associated standardized terminologies.”