Rave Harpaz

Summary

Affiliation: Columbia University
Country: USA

Publications

  1. pmc Novel data-mining methodologies for adverse drug event discovery and analysis
    R Harpaz
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA
    Clin Pharmacol Ther 91:1010-21. 2012
  2. pmc Mining multi-item drug adverse effect associations in spontaneous reporting systems
    Rave Harpaz
    Department of Biomedical Informatics, Columbia University, 622 West 168th St, VC5, New York, NY 10032, USA
    BMC Bioinformatics 11:S7. 2010
  3. pmc Biclustering of adverse drug events in the FDA's spontaneous reporting system
    R Harpaz
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA
    Clin Pharmacol Ther 89:243-50. 2011
  4. pmc Drug-drug interaction through molecular structure similarity analysis
    Santiago Vilar
    Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
    J Am Med Inform Assoc 19:1066-74. 2012
  5. pmc Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions
    Rave Harpaz
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA
    J Am Med Inform Assoc 20:413-9. 2013
  6. pmc A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations
    Wei Wang
    Dept of Biomedical Informatics, Columbia University, New York, NY, USA
    AMIA Annu Symp Proc 2011:1464-70. 2011
  7. pmc Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis
    Santiago Vilar
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York 10032, USA
    J Am Med Inform Assoc 18:i73-80. 2011
  8. pmc Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis
    Santiago Vilar
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, United States of America
    PLoS ONE 7:e41471. 2012
  9. pmc Determining the reasons for medication prescriptions in the EHR using knowledge and natural language processing
    Ying Li
    Department of Biomedical Informatics, Columbia University, New York, NY, USA
    AMIA Annu Symp Proc 2011:768-76. 2011
  10. pmc Statistical Mining of Potential Drug Interaction Adverse Effects in FDA's Spontaneous Reporting System
    Rave Harpaz
    Dept of Biomedical Informatics, Columbia University, New York, NY
    AMIA Annu Symp Proc 2010:281-5. 2010

Collaborators

Detail Information

Publications10

  1. pmc Novel data-mining methodologies for adverse drug event discovery and analysis
    R Harpaz
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA
    Clin Pharmacol Ther 91:1010-21. 2012
    ..This article provides an overview of recent methodological innovations and data sources used to support ADE discovery and analysis...
  2. pmc Mining multi-item drug adverse effect associations in spontaneous reporting systems
    Rave Harpaz
    Department of Biomedical Informatics, Columbia University, 622 West 168th St, VC5, New York, NY 10032, USA
    BMC Bioinformatics 11:S7. 2010
    ....
  3. pmc Biclustering of adverse drug events in the FDA's spontaneous reporting system
    R Harpaz
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA
    Clin Pharmacol Ther 89:243-50. 2011
    ....
  4. pmc Drug-drug interaction through molecular structure similarity analysis
    Santiago Vilar
    Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
    J Am Med Inform Assoc 19:1066-74. 2012
    ..Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs...
  5. pmc Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions
    Rave Harpaz
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA
    J Am Med Inform Assoc 20:413-9. 2013
    ..We claim that this approach leads to improved accuracy of signal detection when the goal is to produce a highly selective ranked set of candidate ADRs...
  6. pmc A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations
    Wei Wang
    Dept of Biomedical Informatics, Columbia University, New York, NY, USA
    AMIA Annu Symp Proc 2011:1464-70. 2011
    ..For further proof of concept this method was applied to 48 drugs to determine whether they caused another AE, myocardial infarction. Results showed that AUROC was 0.93 and 0.86 respectively...
  7. pmc Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis
    Santiago Vilar
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York 10032, USA
    J Am Med Inform Assoc 18:i73-80. 2011
    ..Various data mining approaches have been developed that use AERS to detect signals identifying associations between drugs and ADE. The signals must then be monitored further by domain experts, which is a time-consuming task...
  8. pmc Enhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitis
    Santiago Vilar
    Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, United States of America
    PLoS ONE 7:e41471. 2012
    ..Although different data mining approaches have been shown to be valuable, it is still crucial to improve the quality of the generated signals...
  9. pmc Determining the reasons for medication prescriptions in the EHR using knowledge and natural language processing
    Ying Li
    Department of Biomedical Informatics, Columbia University, New York, NY, USA
    AMIA Annu Symp Proc 2011:768-76. 2011
    ..Future work will focus on increasing the accuracy and coverage of the indication knowledge and evaluating its performance using a much larger set of drugs frequently used in the outpatient population...
  10. pmc Statistical Mining of Potential Drug Interaction Adverse Effects in FDA's Spontaneous Reporting System
    Rave Harpaz
    Dept of Biomedical Informatics, Columbia University, New York, NY
    AMIA Annu Symp Proc 2010:281-5. 2010
    ..This paper examines the application of a highly optimized and tailored implementation of the Apriori algorithm, as well as methods addressing data quality issues, to the identification of DIAEs in FDAs SRS...