A detailed-up of a smartphone display screen displaying social media app icons, specifically Reddit. Credit score: Ralph Olazo, Unsplash (CC0, creativecommons.org/publicdomain/zero/1.0/)
A brand new synthetic intelligence device can scan social media knowledge to find hostile occasions related to shopper well being merchandise, in accordance with a research revealed within the open-access journal PLOS Digital Well being by John Ayers of the College of California, San Diego, U.S., and colleagues.
The fixed post-market surveillance of the security of shopper merchandise is essential for public well being and security. Nonetheless, present adverse-event (AE) reporting programs for permitted prescription drugs and medical gadgets depend upon voluntary submissions from medical doctors and producers to the U.S. Meals and Drug Administration.
The fast development in shopper well being merchandise, akin to cannabis-derived merchandise and dietary dietary supplements, has led to the necessity for brand spanking new AE detection programs.
Within the new research, researchers examined the efficacy of a brand new automated machine studying device, “Waldo,” that may sift by social media textual content to search out shopper descriptions of hostile occasions. The device was examined on its capacity to scan Reddit posts to search out AEs of cannabis-derived merchandise.
When in comparison with human AE annotations of a set of Reddit posts, Waldo had an accuracy of 99.7%, much better than a general-purpose ChatGPT chatbot that was given the identical set of posts.
In a broader dataset of 437,132 Reddit posts, Waldo recognized 28,832 potential experiences of hurt. When the researchers manually validated a random pattern of those posts, they discovered that 86% have been true AEs. The workforce has made Waldo open-source in order that anybody—researchers, clinicians, or regulators—can use it.
“Waldo represents a significant advancement in social media-based AE detection, achieving superior performance compared to existing approaches,” the authors say. “Additionally, Waldo’s automated approach has broad applicability beyond cannabis-derived products to other consumer health products that similarly lack regulatory oversight.”
Lead writer Karan Desai says, “Waldo shows that the health experiences people share online are not just noise, they’re valuable safety signals. By capturing these voices, we can surface real-world harms that are invisible to traditional reporting systems.”
John Ayers provides, “This project highlights how digital health tools can transform post-market surveillance. By making Waldo open-source, we’re ensuring that anyone, from regulators to clinicians, can use it to protect patients.”
Second writer, Vijay Tiyyala, notes, “From a technical perspective, we demonstrated that a carefully trained model like RoBERTa can outperform state-of-the-art chatbots for AE detection. Waldo’s accuracy was surprising and encouraging.”
“By democratizing access to Waldo, the team hopes to accelerate open science and improve safety for patients.”
Extra data:
Desai KS, et al. Waldo: Automated discovery of hostile occasions from unstructured self reportsPLOS Digital Well being (2025). DOI: 10.1371/journal.pdig.0001011
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