DIVA: Exploration and Validation of Hypothesized Drug‐Drug Interactions

Abstract

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. DTVA'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.

ChemoGraph: Interactive Visual Exploration of the Chemical Space

“…Approaches that use networks leverage interactive visualizations that support exploratory workflows. Kakar et al [KQR * 19] claim that networks tend to show association relationships better. They created a visual analytics system, DIVA, to analyze candidate drug interaction signals via coordinated views of force‐directed graphs and tree views.…”

Explaining artificial intelligence with visual analytics in healthcare

“…Hovering one part of a visualization can highlight other parts in the same visualization, for example connected ribbons and sets in parallel sets (e.g., Abdullah et al, 2020), neighbors in a network (Brunker et al, 2019;L'Yi et al, 2017), or other features from the hovered data point (Cao et al, 2011;Gotz et al, 2011, Figure 4a). Hovering can also highlight related entities across multiple visualizations (e.g., Kakar et al, 2019;Kumar et al, 2015).…”

“…Select interactions mark data items, either manually through brushing (e.g., S. Guo et al, 2018), lasso selection (e.g., Kwon et al, 2019;Raidou, Kuijf, et al, 2016;Figure 3e), clicking on a legend (R. Guo et al, 2020), or pinning (Dingen et al, 2019;Kakar et al, 2019); or automatically based on a chosen metric (e.g., Stolper et al, 2014). Selected data are typically colored prominently to easily focus on them.…”