OHDSI Home | Forums | Wiki | Github

Weekly OHDSI Digest - 23May2016

OHDSI MEETINGS THIS WEEK

Patient Visualization Work Group Meeting - Tuesday at 2pm ET

ANNOUNCEMENTS

OHDSI Symposium 2016 - SAVE THE DATE
Mark your calendars! The second annual OHDSI Symposium will take place on Friday, September 23rd 2016 at the Washington Hilton in Washington DC. Registration will open shortly:

OHDSI Symposium 2016 - Call for participation
The symposium organizing committee is now accepting submission abstracts for posters or software demonstrations to be presented during the OHDSI Collaborator Showcase at the symposium.
Deadline to submit abstracts - June 15th 2016
http://www.ohdsi.org/ohdsi-symposium-2016-call-for-participation/

NEW OHDSI NETWORK STUDY - Levetiracetam and Risk of Angioedema in patients with Seizure Disorder
We are pleased to announce the official start of the Keppra and Angioedema study! More details are available on the wiki:
http://www.ohdsi.org/web/wiki/doku.php?id=research:angioedema
And Github:

COMMUNITY PUBLICATIONS

A curated and standardized adverse drug event resource to accelerate drug safety research
Authors: @Juan_Banda, @lee_evans, Rami S. Vanguri, @nick, @Patrick_Ryan, @nigam
http://www.nature.com/articles/sdata201626
Abstract:
Identification of adverse drug reactions (ADRs) during the post-marketing phase is one of the most important goals of drug safety surveillance. Spontaneous reporting systems (SRS) data, which are the mainstay of traditional drug safety surveillance, are used for hypothesis generation and to validate the newer approaches. The publicly available US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) data requires substantial curation before they can be used appropriately, and applying different strategies for data cleaning and normalization can have material impact on analysis results. We provide a curated and standardized version of FAERS removing duplicate case records, applying standardized vocabularies with drug names mapped to RxNorm concepts and outcomes mapped to SNOMED-CT concepts, and pre-computed summary statistics about drug-outcome relationships for general consumption. This publicly available resource, along with the source code, will accelerate drug safety research by reducing the amount of time spent performing data management on the source FAERS reports, improving the quality of the underlying data, and enabling standardized analyses using common vocabularies.

A data-driven concept schema for defining clinical research data needs

t