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ARXIV:2603.26544 · PHARMACOVIGILANCE AI · SUBMITTED 30 MAR · 21:52 UTC · FRESHNESS STALE
ARXIV:2603.26544PHARMACOVIGILANCE AISUBMITTED 30 MAR · 21:52 UTCFRESHNESS STALEMaria Kefala · Jeffery L. Painter · Syed Tauhid Bukhari · Maurizio Sessa · arXiv
A time-indexed EU drug safety dataset with regulatory metadata to benchmark AI signal detection methods.
Opportunity summary
Pain A time-indexed EU drug safety dataset with regulatory metadata to benchmark AI signal detection methods.
Evidence 0 refs | 3 sources | 33% coverage
Blocker Evidence unverified
A time-indexed EU drug safety dataset with regulatory metadata to benchmark AI signal detection methods. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses…
Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results: The database includes 17,763 SmPC versions spanning 1995-2025, comprising 125,026 drug-AE associations. Code availability is flagged in the production record; the public repository…
Pharmacovigilance AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A time-indexed EU drug safety dataset with regulatory metadata to benchmark AI signal detection methods.
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10.48550/arXiv.2603.26544A time-indexed EU drug safety dataset with regulatory metadata to benchmark AI signal detection methods.
Abstract
Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods and limiting evaluation of early detection performance. This study addresses this gap by developing a time-indexed reference dataset for the European Union (EU), incorporating the timing of AE inclusion in product labels along with regulatory metadata. Methods: Current and historical Summaries of Product Characteristics (SmPCs) for all centrally authorized products (n=1,513) were retrieved from the EU Union Register of Medicinal Products (data lock: 15 December 2025). Section 4.8 was extracted and processed using DeepSeek V3 to identify AEs. Regulatory metadata, including labelling changes, were programmatically extracted. Time indexing was based on the date of AE inclusion in the SmPC. Results: The database includes 17,763 SmPC versions spanning 1995-2025, comprising 125,026 drug-AE associations. The time-indexed reference dataset, restricted to active products, included 1,479 medicinal products and 110,823 drug-AE associations. Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%). Safety updates peaked around 2012. Gastrointestinal, skin, and nervous system disorders were the most represented System Organ Classes. Drugs had a median of 48 AEs across 14 SOCs. Conclusions: The proposed dataset addresses a critical gap in pharmacovigilance by incorporating temporal information on AE recognition for the EU, supporting more accurate assessment of signal detection performance and facilitating methodological comparisons across analytical approaches.
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Proof status
unverified0 refs; 3 sources; 33% coverage.
What was readable
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PROBLEM
A time-indexed EU drug safety dataset with regulatory metadata to benchmark AI signal detection methods. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-confirmation periods...
METHOD
Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs) are officially recognized by regulatory authorities, preventing restriction of analyses to pre-co...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results: The database includes 17,763 SmPC versions spanning 1995-2025, comprising 125,026 drug-AE associations. Code availability is flagged in the production record; the public repository link still nee...
WHY NOW
Pharmacovigilance AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Development of a European Union Time-Indexed Reference Dataset for Assessing the Performance of Signal Detection Methods in Pharmacovigilance using a Large Language Model
This is the central theme and stated purpose of the study, clearly articulated in the title and abstract.
partial
This study addresses this gap by developing a time-indexed reference dataset for the European Union (EU), incorporating the timing of AE inclusion in product labels along with regulatory metadata.
The abstract explicitly states this as a key feature of the developed dataset.
partial
Section 4.8 was extracted and processed using DeepSeek V3 to identify AEs.
The abstract and methods section clearly state the LLM used and its function.
partial
The database includes 17,763 SmPC versions spanning 1995-2025, comprising 125,026 drug-AE associations.
Specific quantitative results are provided in the abstract.
partial
The time-indexed reference dataset, restricted to active products, included 1,479 medicinal products and 110,823 drug-AE associations.
Specific quantitative results are provided in the abstract.
partial
Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%).
Specific quantitative results are provided in the abstract.
partial
Safety updates peaked around 2012.
A specific temporal result is stated in the abstract.
partial
The proposed dataset addresses a critical gap in pharmacovigilance by incorporating temporal information on AE recognition for the EU, supporting more accurate assessment of signal detection performance and facilitating methodological comparisons across analytical approaches.
This is a concluding statement in the abstract summarizing the significance of the work.
partial
Most AEs were identified pre-marketing (74.5%) versus post-marketing (25.5%).
The abstract presents a clear statistical finding regarding the timing of AE identification.
partial
Development of a European Union Time-Indexed Reference Dataset for Assessing the Performance of Signal Detection Methods in Pharmacovigilance using a Large Language Model
This is the central theme and stated purpose of the study, explicitly mentioned in the title and abstract.
partial
This study addresses this gap by developing a time-indexed reference dataset for the European Union (EU), incorporating the timing of AE inclusion in product labels along with regulatory metadata.
The abstract clearly states the dataset's composition, including the temporal aspect of AE inclusion and regulatory metadata.
partial
Section 4.8 was extracted and processed using DeepSeek V3 to identify AEs.
The abstract explicitly mentions the LLM used and its function in extracting AEs from SmPCs.
partial
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A time-indexed EU drug safety dataset with regulatory metadata to benchmark AI signal detection methods.
Segment
Pharmacovigilance AI
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1/3 checks · 33%
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0 references, 3 sources, 33% evidence coverage.
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Classify regulatory flags before commercialization planning.
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Regulatory need unclassified.
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