Rave Harpaz

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Rave Harpaz

Rave Harpaz, PhD
Senior Research Scientist
Oracle Health Sciences

Rave Harpaz is a Senior Research Scientist at Oracle Health Sciences. Previously, Rave was a Research Scientist at the Center for Biomedical Informatics Research, Stanford University (2012- 2014), a post-doctoral fellow at the Department of Biomedical Informatics Columbia University (2009-2012), and a quantitative risk modeling analyst at Merrill Lynch (2007-2009). Rave holds a PhD in Computer Science from the City University of New York (2008) specializing in the area of unsupervised Machine Learning, and holds a Law degree (LLB) from Tel-Aviv University, Israel.

Rave’s current research is focused on methods to extract and combine drug safety information from several data sources including: health records, spontaneous reports, the biomedical literature, biological databases, and the social media.

Harpaz R, DuMouchel W, Shah NH. Comment on: “Zoo or Savannah? Choice of Training Ground for Evidence-Based Pharmacovigilance”. Drug Safety 2015, 38(1), pp 113-114

Harpaz R, Callahan A, Tamang S, Low Y, Odgers D, Finlayson S, Jung K, LePendu P, Shah NH. Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art. Drug Safety 2014. DOI 10.1007/s40264-014-0218-z

Boyce RD, Ryan PB, Noren GN, et al. Bridging Islands of Information to Establish an Integrated Knowledge Base of Drugs and Health Outcomes of Interest. Drug Saf. 2014 Jul 2;2:2.

Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther. 2012 Jun;91(6):1010-21. doi: 10.38/clpt.2012.50.

Harpaz R, Odgers D, Gaskin G, et al. A Time-indexed Reference Standard of Adverse Drug Reactions. Nature Scientific Data 2014; 1:140043, DOI: 10.1038/sdata.2014.43.

White RW, Harpaz R, Shah NH, DuMouchel W, Horvitz E. Toward Enhanced Pharmacovigilance Using Patient-Generated Data on the Internet Nature – Clinical Pharmacology & Therapeutics 2014, doi: 10.1038/clpt.2014.77 (shared first authorship)

Harpaz R, DuMouchel W, LePendu P, Shah NH. Empirical Bayes Model to Combine Signals of Adverse Drug Reactions. Proc. of 2013 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’13), pp 1339-1347.

Harpaz R, DuMouchel W, LePendu P, Bauer-Mehren A, Ryan P, Shah NH. Performance of Pharmacovigilance Signal Detection Algorithms for the FDA Adverse Event Reporting System. Nature – Clinical Pharmacology & Therapeutics 2013, 93(6), pp 539-546.

LePendu P , Iyer SV, Bauer-Mehren A, Harpaz R, Mortensen JM, Podchiyska T, Farris TA, Shah NH. Pharmacovigilance Using Clinical Notes. Nature – Clinical Pharmacology & Therapeutics 2013, 93(6), pp 547-555.

Harpaz R, Vilar S, DuMouchel W, Salmasian H, Haerian K, Chase HS, Friedman C. Combining Signals from Spontaneous Reports and Electronic Health Records for Detection of Adverse Drug Reactions. Journal of the American Medical Informatics Association 2012, 0:1–7, doi:10.1136/amiajnl-2012-000930.

Vilar S, Harpaz R, Chase HS, Costanzi S, Rabadan R, Friedman C. Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis. Journal of the American Medical Informatics Association, 2011, 18 Suppl 1:i73-80.

Harpaz R, Perez H, Chase HS, Rabadan R, Hripcsak G, Friedman C. Biclustering of Adverse Drug Events in FDA’s Spontanous Reporting System. Nature – Clinical Pharmacology & Therapeutics, 2011, 89(2), pp 243-250.

Harpaz R, Chase HS, Friedman C. Mining Multi-Item Drug Adverse Effect Associations in Spontaneous Reporting Systems. BMC Bioinformatics 2010 11(Suppl 9):S7 [selected among best papers in AMIA’s Translational Bioinformatics Summit 2010].