Ping Zhang  Ping Zhang photo       

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Research Staff Member - Center for Computational Health
IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
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Professional Associations

Professional Associations:  ACM SIGKDD  |  American Medical Informatics Association  |  IEEE Computer Society  |  International Society for Computational Biology


AMIA 2015 Panel

Recent Advances in Computational Drug Repositioning

Panelists: Ping Zhang (IBM T.J. Watson Research Center), Atul Butte (University of California San Francisco), Nigam Shah (Stanford University), Nicholas Tatonetti (Columbia University), Hua Xu (The University of Texas Health Science Center at Houston)

Abstract:

Computational drug repositioning is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path towards new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. Despite the progress simulated by big data analytics, there is clearly room for technical improvement with regard to computational drug repositioning methods. Furthermore, to materialize the true potential and impact of these methods, much work is needed to show that they can be successfully adopted into practical applications. In this panel, participants will summarize the recent advances in computational drug repositioning and identify challenges and opportunities. Panel participants will synthesize their perspectives on these key issues and likely future developments in this area, explore a diverse set of topics, and engage in thoughtful discussion with the audience.

 

AMIA Joint Summits 2017 Panel

Big Data for Pharmacovigilance: Challenge and Opportunity

Panelists: Ping Zhang (IBM T.J. Watson Research Center), Graciela Gonzalez (University of Pennsylvania), Rave Harpaz (Oracle Health Sciences), Ying Li (IBM T.J. Watson Research Center), Nigam Shah (Stanford University)

Abstract:

Adverse drug reactions (ADRs) are a major burden for patients and the healthcare industry. It usually causes preventable hospitalizations and deaths, while associated with a huge amount of cost. Spontaneous reporting systems (SRSs) have been the cornerstone in pharmacovigilance for a long time, and are effective at detecting many types of ADRs. However, significant under-reporting bias inherently leaves patients at risk until sufficient clinical evidence has been granted. To augment the current systems, there are new ways to conduct pharmacovigilance using expanded data sources including data from electronic health records (EHRs), scientific literature and social media. Collectively labeled as the big data, they share the characteristics of large volumes, diversity and complexity that present both challenges and opportunities to its holders. Recently, the research community has devoted much effort to this field that may fundamentally transfer the manner in which ADR can be identified and evaluated. However, there is clearly room for technical improvement with regard to computational drug safety surveillance methods. Furthermore, to materialize the true potential and impact of these methods, much work is needed to show that they can be successfully adopted into practical applications. In this panel, participants will summarize the recent advances in big data for pharmacovigilance and identify challenges and opportunities. Panel participants will synthesize their perspectives on these key issues and vision the future developments in this area, explore a diverse set of topics, and engage in thoughtful discussion with the audience.