Bio

Honors & Awards


  • Research Grant, GlaxoSmithKline (05/2015-05/2016)
  • Tillman Scholar, Pat Tillman Foundation (06/2014-06/2016)

Education & Certifications


  • MS, Stanford University, Biomedical Informatics
  • MS, Johns Hopkins, Biotechnology (2013)

Stanford Advisors


Service, Volunteer and Community Work


  • Military Veteran Volunteer, The Mission Continues (7/1/2015 - Present)

    Location

    Palo Alto, California

  • Military Veteran Volunteer, Team Rubicon (January 2015 - Present)

    Global Disaster Relief

    Location

    Palo Alto, California

  • Pat Tillman Scholar, Pat Tillman Foundation (May 2014 - Present)

    Location

    Chicago, Illinois

Research & Scholarship

Current Clinical Interests


  • Data Mining
  • Data Analyses, Statistical
  • Machine Learning
  • Electronic Health Records
  • Data Systems

Research Projects


  • GlaxoSmithKline Research Collaboration (Scholarly Concentration Project)

    Start Date

    May 2015

    Location

    Stanford, California

    Organization

    Stanford/GlaxoSmithKline Research Collaboration

  • INTREPID Project (Scholarly Concentration Project)

    Linked datamining using Stanford Translational Research Integrated Database Environment EHR data and Biomedical Linked Open Data from around the web to solve complex biomedical problems.

    Time Period

    March 2014 - Present

    Location

    Stanford, California

    Organization

    Stanford University

Lab Affiliations


Professional

Work Experience


  • Principal Researcher, Stanford School of Medicine/GlaxoSmithKline Research Collaboration (5/1/2015 - Present)

    Location

    Stanford, California

  • Research Assistant, Stanford School of Medicine (10/1/2013 - Present)

    Location

    Stanford, California

  • President, Hopkins Biotech Network (February 2012 - June 2013)

    Location

    Baltimore, MD

  • Technology Marketing, Johns Hopkins School of Medicine (February 2012 - October 2012)

    Location

    Baltimore, MD

  • Intelligence, United States Army (June 2007 - March 2013)

    Location

    Washington, DC

Publications

Journal Articles


  • A time-indexed reference standard of adverse drug reactions. Scientific data Harpaz, R., Odgers, D., Gaskin, G., DuMouchel, W., Winnenburg, R., Bodenreider, O., Ripple, A., Szarfman, A., Sorbello, A., Horvitz, E., White, R. W., Shah, N. H. 2014; 1: 140043

    Abstract

    Undetected adverse drug reactions (ADRs) pose a major burden on the health system. Data mining methodologies designed to identify signals of novel ADRs are of deep importance for drug safety surveillance. The development and evaluation of these methodologies requires proper reference benchmarks. While progress has recently been made in developing such benchmarks, our understanding of the performance characteristics of the data mining methodologies is limited because existing benchmarks do not support prospective performance evaluations. We address this shortcoming by providing a reference standard to support prospective performance evaluations. The reference standard was systematically curated from drug labeling revisions, such as new warnings, which were issued and communicated by the US Food and Drug Administration in 2013. The reference standard includes 62 positive test cases and 75 negative controls, and covers 44 drugs and 38 events. We provide usage guidance and empirical support for the reference standard by applying it to analyze two data sources commonly mined for drug safety surveillance.

    View details for DOI 10.1038/sdata.2014.43

    View details for PubMedID 25632348

  • Analyzing search behavior of healthcare professionals for drug safety surveillance. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Odgers, D. J., Harpaz, R., Callahan, A., Stiglic, G., Shah, N. H. 2015; 20: 306-317

    Abstract

    Post-market drug safety surveillance is hugely important and is a significant challenge despite the existence of adverse event (AE) reporting systems. Here we describe a preliminary analysis of search logs from healthcare professionals as a source for detecting adverse drug events. We annotate search log query terms with biomedical terminologies for drugs and events, and then perform a statistical analysis to identify associations among drugs and events within search sessions. We evaluate our approach using two different types of reference standards consisting of known adverse drug events (ADEs) and negative controls. Our approach achieves a discrimination accuracy of 0.85 in terms of the area under the receiver operator curve (AUC) for the reference set of well-established ADEs and an AUC of 0.68 for the reference set of recently labeled ADEs. We also find that the majority of associations in the reference sets have support in the search log data. Despite these promising results additional research is required to better understand users' search behavior, biasing factors, and the overall utility of analyzing healthcare professional search logs for drug safety surveillance.

    View details for PubMedID 25592591

  • Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art DRUG SAFETY Harpaz, R., Callahan, A., Tamang, S., Low, Y., Odgers, D., Finlayson, S., Jung, K., LePendu, P., Shah, N. H. 2014; 37 (10): 777-790

Conference Proceedings


  • Mining Electronic Health Records Using Linked Data American Medical Informatics Association: 2015 Joint Summits on Translational Science Odgers, D., Dumontier, M. 2015

Presentations


  • Analyzing search behavior of healthcare professionals for drug safety surveillance.

    Post-market drug safety surveillance is hugely important and is a significant challenge despite the existence
    of adverse event (AE) reporting systems. Here we describe a preliminary analysis of search logs from
    healthcare professionals as a source for detecting adverse drug events. We annotate search log query terms
    with biomedical terminologies for drugs and events, and then perform a statistical analysis to identify
    associations among drugs and events within search sessions. We evaluate our approach using two different
    types of reference standards consisting of known adverse drug events (ADEs) and negative controls. Our
    approach achieves a discrimination accuracy of 0.85 in terms of the area under the receiver operator curve
    (AUC) for the reference set of well-established ADEs and an AUC of 0.68 for the reference set of recently
    labeled ADEs. We also find that the majority of associations in the reference sets have support in the search
    log data. Despite these promising results additional research is required to better understand users’ search
    behavior, biasing factors, and the overall utility of analyzing healthcare professional search logs for drug
    safety surveillance.

    Time Period

    January 2015

    Presented To

    Pacific Symposium on Biocomputing

    Location

    Waimea, Hawaii

  • Mining Electronic Health Records using Linked Data.

    Time Period

    March 2015

    Presented To

    American Medical Informatics Association: 2015 Joint Summits on Translational Science

    Location

    San Fransisco, California

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