How do others cope? Extracting coping strategies for adverse drug events from social media.

Autor: Dirkson A; Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, Netherlands. Electronic address: a.r.dirkson@liacs.leidenuniv.nl., Verberne S; Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, Netherlands. Electronic address: s.verberne@liacs.leidenuniv.nl., van Oortmerssen G; Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, Netherlands. Electronic address: g.van.oortmerssen@liacs.leidenuniv.nl., Gelderblom H; Department of Medical Oncology, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA Leiden, Netherlands. Electronic address: A.J.Gelderblom@lumc.nl., Kraaij W; Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA Leiden, Netherlands. Electronic address: w.kraaij@liacs.leidenuniv.nl.
Jazyk: angličtina
Zdroj: Journal of biomedical informatics [J Biomed Inform] 2023 Mar; Vol. 139, pp. 104228. Date of Electronic Publication: 2022 Oct 26.
DOI: 10.1016/j.jbi.2022.104228
Abstrakt: Patients advise their peers on how to cope with their illness in daily life on online support groups. To date, no efforts have been made to automatically extract recommended coping strategies from online patient discussion groups. We introduce this new task, which poses a number of challenges including complex, long entities, a large long-tailed label space, and cross-document relations. We present an initial ontology for coping strategies as a starting point for future research on coping strategies, and the first end-to-end pipeline for extracting coping strategies for side effects. We also compared two possible computational solutions for this novel and highly challenging task; multi-label classification and named entity recognition (NER) with entity linking (EL). We evaluated our methods on the discussion forum from the Facebook group of the worldwide patient support organization 'GIST support international' (GSI); GIST support international donated the data to us. We found that coping strategy extraction is difficult and both methods attain limited performance (measured with F 1 score) on held out test sets; multi-label classification outperforms NER+EL (F 1 =0.220 vs F 1 =0.155). An inspection of the multi-label classification output revealed that for some of the incorrect predictions, the reference label is close to the predicted label in the ontology (e.g. the predicted label 'juice' instead of the more specific reference label 'grapefruit juice'). Performance increased to F 1 =0.498 when we evaluated at a coarser level of the ontology. We conclude that our pipeline can be used in a semi-automatic setting, in interaction with domain experts to discover coping strategies for side effects from a patient forum. For example, we found that patients recommend ginger tea for nausea and magnesium and potassium supplements for cramps. This information can be used as input for patient surveys or clinical studies.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: