{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16(1)"],"submitter":["Higgins K"],"pubmed_abstract":["Helicobacter pylori (H. pylori) is the most common carcinogenic pathogen globally and the leading cause of gastric cancer. Here, we develop a reinforcement learning-based AI Clinician system to personalise treatment selection and evaluate its ability to improve eradication success compared to clinician-prescribed therapies. The model is trained and internally validated on 38,049 patients from the retrospective European Registry on Helicobacter pylori Management (Hp-EuReg), using independent state deep Q-learning (isDQN) to recommend optimal therapies based on patient characteristics such as age, sex, antibiotic allergies, country, and pre-treatment indication. In internal validation using real-world Hp-EuReg data, AI-recommended therapies achieve a 94.1% success rate (95% CI: 93.2-95.0%) v"],"journal":["Nature communications"],"pagination":["6472"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12259899"],"repository":["biostudies-literature"],"pubmed_title":["The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations."],"pmcid":["PMC12259899"],"pubmed_authors":["Laponogov I","Higgins K","Martins A","Pescino JA","Matysiak-Budnik T","Marco AM","Southern J","Cabeza-Segura M","Leja M","Fernandez-Esparrach G","Polaka I","Martin J","Kupcinskas J","Coimbra M","Pereira AC","Castillo J","Honing J","Ferreira RM","Veselkov D","Marti EJ","Maravic Z","Figueiredo C","Spaander MCW","Fontes F","Sedola S","Carneiro F","Dinis-Ribeiro M","Cuatrecasas M","AIDA CONSORTIUM","Barros R","Jonaitis P","Veselkov K","Moreira L","Kanonnikoff TF","Jonaitis L","Nyssen OP","Gisbert JP"],"additional_accession":[]},"is_claimable":false,"name":"The Helicobacter pylori AI-clinician harnesses artificial intelligence to personalise H. pylori treatment recommendations.","description":"Helicobacter pylori (H. pylori) is the most common carcinogenic pathogen globally and the leading cause of gastric cancer. Here, we develop a reinforcement learning-based AI Clinician system to personalise treatment selection and evaluate its ability to improve eradication success compared to clinician-prescribed therapies. The model is trained and internally validated on 38,049 patients from the retrospective European Registry on Helicobacter pylori Management (Hp-EuReg), using independent state deep Q-learning (isDQN) to recommend optimal therapies based on patient characteristics such as age, sex, antibiotic allergies, country, and pre-treatment indication. In internal validation using real-world Hp-EuReg data, AI-recommended therapies achieve a 94.1% success rate (95% CI: 93.2-95.0%) v","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jul","modification":"2026-07-15T08:57:59.323Z","creation":"2026-07-03T03:10:07.407Z"},"accession":"S-EPMC12259899","cross_references":{"pubmed":["40659612"],"doi":["10.1038/s41467-025-61329-5"]}}