ArticleDrug safety2021
Augmenting Product Defect Surveillance Through Web Crawling and Machine Learning in Singapore.
Article in Drug safety, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Artificial intelligence and computational methods in the Asia-Pacific pharmacovigilance landscape: a systematic review.International journal of public health · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionSubstandard medicines are medicines that fail to meet their quality standards and/or specifications. Substandard medicines can lead to serious safety issues affecting public health. With the increasing number of pharmaceuticals and the complexity of the pharmaceutical manufacturing supply chain, monitoring for substandard medicines via manual environmental scanning can be laborious and time consuming.
methodsA web crawler was developed to automatically detect and extract alerts on substandard medicines published on the Internet by regulatory agencies. The crawled data were labelled as related to substandard medicines or not. An expert-derived keyword-based classification algorithm was compared against machine learning algorithms to identify substandard medicine alerts on two validation datasets (n = 4920 and n = 2458) from a later time period than training data. Models were comparatively assessed for recall, precision and their F1 scores (harmonic mean of precision and recall).
resultsThe web crawler routinely extracted alerts from the 46 web pages belonging to nine regulatory agencies. From October 2019 to May 2020, 12,156 unique alerts were crawled of which 7378 (60.7%) alerts were set aside for validation and contained 1160 substandard medicine alerts (15.7%). An ensemble approach of combining machine learning and keywords achieved the best recall (94% and 97%), precision (85% and 80%) and F1 scores (89% and 88%) on temporal validation.
conclusionsCombining robust web crawler programmes with rigorously tested filtering algorithms based on machine learning and keyword models can automate and expand horizon scanning capabilities for issues relating to substandard medicines.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.