Browsing by Author "Harrison, Lane"

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    DIVA: Exploration and Validation of Hypothesized Drug-Drug Interactions
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Kakar, Tabassum; Qin, Xiao; Rundensteiner, Elke A.; Harrison, Lane; Sahoo, Sanjay K.; De, Suranjan; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Adverse reactions caused by drug-drug interactions are a major public health concern. Currently, adverse reaction signals are detected through a tedious manual process in which drug safety analysts review a large number of reports collected through post-marketing drug surveillance. While computational techniques in support of this signal analysis are necessary, alone they are not sufficient. In particular, when machine learning techniques are applied to extract candidate signals from reports, the resulting set is (1) too large in size, i.e., exponential to the number of unique drugs and reactions in reports, (2) disconnected from the underlying reports that serve as evidence and context, and (3) ultimately requires human intervention to be validated in the domain context as a true signal warranting action. In this work, we address these challenges though a visual analytics system, DIVA, designed to align with the drug safety analysis workflow by supporting the detection, screening, and verification of candidate drug interaction signals. DIVA's abstractions and encodings are informed by formative interviews with drug safety analysts. DIVA's coordinated visualizations realize a proposed novel augmented interaction data model (AIM) which links signals generated by machine learning techniques with domain-specific metadata critical for signal analysis. DIVA's alignment with the drug review process allows an analyst to interactively screen for important signals, triage signals for in-depth investigation, and validate signals by reviewing the underlying reports that serve as evidence. The evaluation of DIVA encompasses case-studies and interviews by drug analysts at the US Food and Drug Administration - both of which confirm that DIVA indeed is effective in supporting analysts in the critical task of exploring and verifying dangerous drug-drug interactions.
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    SumRe: Design and Evaluation of a Gist-based Summary Visualization for Incident Reports Triage
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Kakar, Tabassum; Qin, Xiao; La, Thang; Sahoo, Sanjay K.; De, Suranjan; Rundensteiner, Elke A.; Harrison, Lane; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Incident report triage is a common endeavor in many industry sectors, often coupled with serious public safety implications. For example, at the US Food and Drug Administration (FDA), analysts triage an influx of incident reports to identify previously undiscovered drug safety problems. However, these analysts currently conduct this critical yet error-prone incident report triage using a generic table-based interface, with no formal support. Visualization design, task-characterization methodologies, and evaluation models offer several possibilities for better supporting triage workflows, including those dealing with drug safety and beyond. In this work, we aim to elevate the work of triage through a task-abstraction activity with FDA analysts. Second, we design an alternative gist-based summary of text documents used in triage (SumRe). Third, we conduct a crowdsourced evaluation of SumRe with medical experts. Results of the crowdsourced study with medical experts (n = 20) suggest that SumRe better supports accuracy in understanding the gist of a given report, and in identifying important reports for followup activities. We discuss implications of these results, including design considerations for triage workflows beyond the drug domain, as well as methodologies for comparing visualization-enabled text summaries.