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A data science approach to drug safety: Semantic and visual mining of adverse drug events from clinical trials of pain treatments

Abstract : Clinical trials are the basis of Evidence-Based Medicine. Trial results are reviewed by experts and consensus panels for producing meta-analyses and clinical practice guidelines. However, reviewing these results is a long and tedious task, hence the meta-analyses and guidelines are not updated each time a new trial is published. Moreover, the independence of experts may be difficult to appraise. On the contrary, in many other domains, including medical risk analysis, the advent of data science, big data and visual analytics allowed moving from expert-based to fact-based knowledge. Since 12 years, many trial results are publicly available online in trial registries. Nevertheless, data science methods have not yet been applied widely to trial data. In this paper, we present a platform for analyzing the safety events reported during clinical trials and published in trial registries. This platform is based on an ontological model including 582 trials on pain treatments, and uses semantic web technologies for querying this dataset at various levels of granularity. It also relies on a 26-dimensional flower glyph for the visualization of the Adverse Drug Events (ADE) rates in 13 categories and 2 levels of seriousness. We illustrate the interest of this platform through several use cases and we were able to find back conclusions that were initially found during meta-analyses. The platform was presented to four experts in drug safety, and is publicly available online, with the ontology of pain treatment ADE.
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https://hal.archives-ouvertes.fr/hal-03201049
Contributor : Jean-Baptiste Lamy <>
Submitted on : Saturday, April 17, 2021 - 2:24:38 PM
Last modification on : Friday, July 2, 2021 - 11:20:43 AM

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Jean-Baptiste Lamy. A data science approach to drug safety: Semantic and visual mining of adverse drug events from clinical trials of pain treatments. Artificial Intelligence in Medicine, Elsevier, 2021, pp.102074. ⟨10.1016/j.artmed.2021.102074⟩. ⟨hal-03201049⟩

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