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

Benjamin Skov Kaas-Hansen*, Davide Placido, Cristina Leal Rodríguez, Hans-Christian Thorsen-Meyer, Simona Gentile, Anna Pors Nielsen, Søren Brunak, Gesche Jürgens, Stig Ejdrup Andersen

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskriftArtikelForskningpeer review

Abstract

We sought to craft a drug safety signalling pipeline associating latent information in clinical free text with exposures to single drugs and drug pairs. Data arose from 12 secondary and tertiary public hospitals in two Danish regions, comprising approximately half the Danish population. Notes were operationalised with a fastText embedding, based on which we trained 10 270 neural-network models (one for each distinct single-drug/drug-pair exposure) predicting the risk of exposure given an embedding vector. We included 2 905 251 admissions between May 2008 and June 2016, with 13 740 564 distinct drug prescriptions; the median number of prescriptions was 5 (IQR: 3-9) and in 1 184 340 (41%) admissions patients used ≥5 drugs concomitantly. A total of 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. Sixteen (14%) of the 115 drug-pair signals were possible interactions, and two (1.7%) were known. In conclusion, we built a language-agnostic pipeline for mining associations between free-text information and medication exposure without manual curation, predicting not the likely outcome of a range of exposures but also the likely exposures for outcomes of interest. Our approach may help overcome limitations of text mining methods relying on curated data in English and can help leverage non-English free text for pharmacovigilance.

OriginalsprogEngelsk
Sider (fra-til)282-293
Antal sider12
TidsskriftBasic & clinical pharmacology & toxicology
Vol/bind131
Udgave nummer4
Tidlig onlinedato26 jul. 2022
DOI
StatusUdgivet - okt. 2022

Bibliografisk note

© 2022 The Authors. Basic & Clinical Pharmacology & Toxicology published by John Wiley & Sons Ltd on behalf of Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).

Fingeraftryk

Udforsk hvilke forskningsemner 'Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records' indeholder.

Citationsformater