Honors & Awards


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

Education & Certifications


  • MS, Stanford University, Management Science and Engineering
  • MS, Stanford University, Biomedical Informatics (2016)
  • MS, Johns Hopkins, Biotechnology (2013)

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

Lab Affiliations


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

All Publications


  • 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

  • Mining Electronic Health Records Using Linked Data American Medical Informatics Association: 2015 Joint Summits on Translational Science Odgers, D., Dumontier, M. 2015
  • Mining Electronic Health Records using Linked Data. AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science Odgers, D. J., Dumontier, M. 2015; 2015: 217-221

    Abstract

    Meaningful Use guidelines have pushed the United States Healthcare System to adopt electronic health record systems (EHRs) at an unprecedented rate. Hospitals and medical centers are providing access to clinical data via clinical data warehouses such as i2b2, or Stanford's STRIDE database. In order to realize the potential of using these data for translational research, clinical data warehouses must be interoperable with standardized health terminologies, biomedical ontologies, and growing networks of Linked Open Data such as Bio2RDF. Applying the principles of Linked Data, we transformed a de-identified version of the STRIDE into a semantic clinical data warehouse containing visits, labs, diagnoses, prescriptions, and annotated clinical notes. We demonstrate the utility of this system though basic cohort selection, phenotypic profiling, and identification of disease genes. This work is significant in that it demonstrates the feasibility of using semantic web technologies to directly exploit existing biomedical ontologies and Linked Open Data.

    View details for PubMedID 26306276

  • 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
  • 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

    Abstract

    Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

    View details for DOI 10.1007/s40264-014-0218-z

    View details for PubMedID 25151493