{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T19:00:16Z","timestamp":1790622016341,"version":"4.1.0"},"reference-count":17,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Br J Clin Pharmacol"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>Machine learning (ML) applications in pharmacovigilance remain limited and underexplored. Using data from the French National pharmacovigilance database (FNPV), this proof\u2010of\u2010concept study aimed to assess the feasibility of using a ML algorithm\u2014eXtreme Gradient Boosting (XGBoost)\u2014combined with SHapley Additive exPlanations (SHAP) analysis, to classify a well\u2010defined drug\u2013adverse reaction pair (warfarin\u2010associated gastrointestinal bleeding) and identify risk factors for reporting this adverse reaction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We extracted individual case safety reports (ICSRs) involving warfarin from the FNPV. Following data preprocessing, the dataset was randomly split into a training set (75%) and an independent test set (25%). The XGBoost algorithm was trained to classify reports as either gastrointestinal bleeding or non\u2010gastrointestinal bleeding adverse reactions. Model performance was assessed on the test set using multiple metrics: accuracy, recall, precision, F1\u2010score and the area under the receiver operating characteristic curve (AUC\u2010ROC). Feature importance and directional effects were interpreted using SHAP values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 2025 ICSRs involving warfarin were extracted from the FNPV. After preprocessing, 1045 ICSRs were retained. On the test set, the XGBoost model demonstrated an accuracy of 0.68, recall of 0.80, precision of 0.68, F1\u2010score of 0.74 and an AUC\u2010ROC of 0.716. SHAP analysis revealed the four most influential features in predicting gastrointestinal bleeding reporting: age, gastrointestinal bleeding risk, antiplatelet and history of renal dysfunction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We demonstrated the feasibility of using an XGBoost algorithm combined with SHAP analysis to pharmacovigilance data for the classification of adverse drug reactions and identify predictive features of reporting, following post hoc model interpretation by pharmacologists.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/bcp.70655","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T04:31:37Z","timestamp":1781757097000},"page":"3503-3512","source":"Crossref","is-referenced-by-count":1,"title":["Applying machine learning to pharmacovigilance data: A proof\u2010of\u2010concept study"],"prefix":"10.1002","volume":"92","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1512-1265","authenticated-orcid":false,"given":"Romain","family":"Barus","sequence":"first","affiliation":[{"name":"Department of Medical and Clinical Pharmacology, Centre of PharmacoVigilance and Pharmacoepidemiology, Faculty of Medicine Toulouse University Hospital  Toulouse France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5274-1062","authenticated-orcid":false,"given":"Pauline","family":"Schiro","sequence":"additional","affiliation":[{"name":"Department of Medical and Clinical Pharmacology, Centre of PharmacoVigilance and Pharmacoepidemiology, Faculty of Medicine Toulouse University Hospital  Toulouse France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0100-4073","authenticated-orcid":false,"given":"Jean\u2010Luc","family":"Faillie","sequence":"additional","affiliation":[{"name":"Department of Medical Pharmacology and Toxicology IDESP, Univ Montpellier, INSERM, CHU Montpellier  Montpellier France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3091-6637","authenticated-orcid":false,"given":"Romane","family":"Freppel","sequence":"additional","affiliation":[{"name":"Department of Medical Pharmacology and Toxicology IDESP, Univ Montpellier, INSERM, CHU Montpellier  Montpellier France"},{"name":"Centre R\u00e9gional de Pharmacovigilance Universit\u00e9 Bourgogne Europe, CHU Dijon Bourgogne  Dijon France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1137-1830","authenticated-orcid":false,"given":"Johana","family":"B\u00e9n\u00e9","sequence":"additional","affiliation":[{"name":"Regional Pharmacovigilance Center, Department of Clinical Pharmacology Lille University Hospital  Lille France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claire","family":"de Canecaude","sequence":"additional","affiliation":[{"name":"Department of Medical and Clinical Pharmacology, Centre of PharmacoVigilance and Pharmacoepidemiology, Faculty of Medicine Toulouse University Hospital  Toulouse France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fran\u00e7ois","family":"Montastruc","sequence":"additional","affiliation":[{"name":"Department of Medical and Clinical Pharmacology, Centre of PharmacoVigilance and Pharmacoepidemiology, Faculty of Medicine Toulouse University Hospital  Toulouse 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