| アブストラクト | Adverse drug reaction (ADR) reporting is a major component of drug safety monitoring; its input will, however, only be optimized if systems can manage to deal with its tremendous flow of information, based primarily on unstructured text fields. The aim of this study was to develop an automated system allowing to code ADRs from patient reports. Our system was based on a knowledge base about drugs, enriched by supervised machine learning (ML) models trained on patients reporting data. To train our models, we selected all cases of ADRs reported by patients to a French Pharmacovigilance Centre through a national web-portal between March 2017 and March 2019 (n = 2,058 reports). We tested both conventional ML models and deep-learning models. We performed an external validation using a dataset constituted of a random sample of ADRs reported to the Marseille Pharmacovigilance Centre over the same period (n = 187). Here, we show that regarding area under the curve (AUC) and F-measure, the best model to identify ADRs was gradient boosting trees (LGBM), with an AUC of 0.93 (0.92-0.94) and F-measure of 0.72 (0.68-0.75). This model was run for external validation showing an AUC of 0.91 and a F-measure of 0.58. We evaluated an artificial intelligence pipeline that was found able to learn how to identify correctly ADRs from unstructured data. This result allowed us to start a new study using more data to further improve our performance and offer a tool that is useful in practice to efficiently manage drug safety information. |
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| ジャーナル名 | Clinical pharmacology and therapeutics |
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| 投稿日 | 2021/4/19 |
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| 投稿者 | Letinier, Louis; Jouganous, Julien; Benkebil, Mehdi; Bel-Letoile, Alicia; Goehrs, Clement; Singier, Allison; Rouby, Franck; Lacroix, Clemence; Miremont, Ghada; Micallef, Joelle; Salvo, Francesco; Pariente, Antoine |
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| 組織名 | INSERM, BPH, U1219, Team Pharmacoepidemiology, Univ. Bordeaux, Bordeaux, France.;CHU de Bordeaux, Pole de Sante Publique, Service de Pharmacologie Medicale,;Centre de Pharmacovigilance de Bordeaux, Bordeaux, France.;Synapse Medicine, Bordeaux, France.;Surveillance Division, Agence nationale de securite du medicament et des produits;de sante (ANSM), Saint Denis, France.;CRPV Marseille Provence Corse, Service Hospitalo-Universitaire de Pharmacologie;Clinique et Pharmacovigilance, Assistance Publique Hopitaux de Marseille,;Marseille, France.;Institut des Neurosciences des Systemes, INSERM 1106, Aix Marseille Universite, |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/33866552/ |
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