DIMACS Workshop on Algorithmic Medical Decision Making: Bridging data sources for drug safety monitoring

May 5, 2011
The Cancer Institute of New Jersey (CINJ)
195 Little Albany Street
New Brunswick, NJ 08903

Ching-Hua Chen-Ritzo, IBM, chenritzo at us.ibm.com
Jianying Hu, IBM Research, jyhu at us.ibm.com
David Madigan, Columbia University, davidbmadigan at gmail.com
Guna Rajagopal, Cancer Inst. of NJ, rajagogu at umdnj.edu
Presented under the auspices of the Special Focus on Algorithmic Decision Theory.

Increasing scientific, regulatory and public scrutiny focuses on the obligation of the medical community, pharmaceutical industry and health authorities to ensure that marketed drugs have acceptable benefit-risk profiles. This is an intricate and ongoing process that begins with carefully designed randomized clinical trials prior to approval but continues after regulatory market authorization when the drug is in widespread clinical use. In the post-approval environment, surveillance schemes based on spontaneous reporting systems (SRSs) represent a cornerstone for the early detection of drug hazards that are novel by virtue of their clinical nature, severity and/or frequency. However, newer data sources such as claims databases, electronic health record databases and social media data have emerged in recent years as important resources. Methods development for these sources currently attracts considerable research attention.

This workshop will discuss the research challenges surrounding the following question: How can we combine information from pre- and post-approval clinical trials, spontaneous reports, and longitudinal observational databases to provide more complete safety profiles at arbitrary moments in time?

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Document last modified on January 24, 2011.