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Scientific Article
unige:146799 
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Automatic Classification of Discharge Letters to Detect Adverse Drug Reactions |
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| Authors | ||
| Published in | Studies in Health Technology and Informatics. 2020, vol. 270, p. 48-52 | |
| Abstract | Adverse drug reactions (ADRs) are frequent and associated to significant morbidity, mortality and costs. Therefore, their early detection in the hospital context is vital. Automatic tools could be developed taking into account structured and textual data. In this paper, we present the methodology followed for the manual annotation and automatic classification of discharge letters from a tertiary hospital. The results show that ADRs and causal drugs are explicitly mentioned in the discharge letters and that machine learning algorithms are efficient for the automatic detection of documents containing mentions of ADRs. | |
| Keywords | Adverse Drug Reaction Reporting Systems — Algorithms — Drug-Related Side Effects and Adverse Reactions — Humans — Patient Discharge — Pharmacovigilance | |
| Identifiers | DOI: 10.3233/SHTI200120 PMID: 32570344 | |
| Full text |
Article (Published version) (244 Kb) - Free access Other version: http://ebooks.iospress.nl/publication/54122 |
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| Structures | ||
| Research groups | Groupe Desmeules Jules (pharmacologie/toxicologie) (567) Interfaces Homme-machine en milieu clinique (610) Pharmaco-omiques et médecine de précision (1003) | |
| Citation (ISO format) | FOUFI, Vasiliki et al. Automatic Classification of Discharge Letters to Detect Adverse Drug Reactions. In: Studies in Health Technology and Informatics, 2020, vol. 270, p. 48-52. doi: 10.3233/SHTI200120 https://archive-ouverte.unige.ch/unige:146799 |
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