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\begin{document}

\title{Language-agnostic pharmacovigilant text mining to elicit side effects
from clinical notes and hospital medication records}



\author[1]{Benjamin Skov Kaas-Hansen}%
\author[2]{Davide Placido}%
\author[2]{Cristina Leal Rodríguez}%
\author[3]{Hans-Christian Thorsen-Meyer}%
\author[4]{Simona Gentile}%
\author[2]{Anna Pors Nielsen}%
\author[2]{Søren Brunak}%
\author[1]{Gesche Jürgens}%
\author[1]{Stig Ejdrup Andersen}%
\affil[1]{Zealand University Hospital Roskilde}%
\affil[2]{University of Copenhagen}%
\affil[3]{Copenhagen University Hospital}%
\affil[4]{Region Zealand}%


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  \date{June 13, 2024}


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\begin{abstract}
Aim: To create a drug safety signalling pipeline associating latent
information in clinical free text with exposure profiles to highlight
potential adverse drug reactions to single drugs and drug pairs.
Methods: All inpatient visits of a 500,000-patient sample from two
Danish regions, between 18 May 2008 and 30 June 2016. Tokens from
clinical notes recorded within 48 hours of admission were
operationalised with a fastText embedding. For each of the 10,720
single-drug and drug-pair exposures from doorstep medication profiles,
we trained a feed-forward neural network predicting the risk of exposure
using embedding vectors as inputs. Results: 2,905,251 inpatient visits
comprised 13,740,564 doorstep drug prescriptions; the median number of
prescriptions was 5 (IQR: 3-9) and in 1,184,340 (41\%) admissions
patients used [?]5 drugs concurrently. 10,788,259 clinical notes were
included, with 179,441,739 tokens retained after pruning. Of 345
single-drug signals reviewed, 28 (8.1\%) represented possibly
undescribed relationships; 186 (54\%) signals were clinically
meaningful. 16 (14\%) of the 115 drug-pair signals were possible
interactions and 2 (1.7\%) were known. Conclusion: We built a
language-agnostic pipeline for mining associations between free-text
information and medication exposure without manual curation, by
predicting not the likely outcome of a range of exposures, but the
likely exposures for outcomes of interest. Our approach may help
overcome limitations of text mining methods relying on curated data in
English and makes our method appealing in settings that must make sense
of non-English free text for pharmacovigilance.%
\end{abstract}%



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