{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11(1)"],"submitter":["Quintero-Villegas A"],"pubmed_abstract":["<h4>Objective</h4>The aim of this study was to identify and analyze all the relevant literature regarding the use of machine learning to predict response to neuromodulation therapies in patients diagnosed with drug-resistant epilepsy.<h4>Material and methods</h4>We systematically search PubMed, Embase, Scopus and Cochrane databases to identify all the studies that used machine learning models to predict response to neuromodulations. Prior to the search, the study was registered at the International prospective register of systematic reviews (PROSPERO, CRD42024543952). Quality assessment and risk of bias was done using PROBAST. A random effects model was used to calculate the pooled value of the AUROC. A sub-analysis was performed for population-specific scenarios.<h4>Results</h4>A total of"],"journal":["Bioelectronic medicine"],"pagination":["27"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12664166"],"repository":["biostudies-literature"],"pubmed_title":["Predicting response to neuromodulation therapies in drug-resistant epilepsy using machine learning models: a meta-analysis and systematic review."],"pmcid":["PMC12664166"],"pubmed_authors":["Quintero-Villegas A","Morales J","Fylaktou F","Zanos TP"],"additional_accession":[]},"is_claimable":false,"name":"Predicting response to neuromodulation therapies in drug-resistant epilepsy using machine learning models: a meta-analysis and systematic review.","description":"<h4>Objective</h4>The aim of this study was to identify and analyze all the relevant literature regarding the use of machine learning to predict response to neuromodulation therapies in patients diagnosed with drug-resistant epilepsy.<h4>Material and methods</h4>We systematically search PubMed, Embase, Scopus and Cochrane databases to identify all the studies that used machine learning models to predict response to neuromodulations. Prior to the search, the study was registered at the International prospective register of systematic reviews (PROSPERO, CRD42024543952). Quality assessment and risk of bias was done using PROBAST. A random effects model was used to calculate the pooled value of the AUROC. A sub-analysis was performed for population-specific scenarios.<h4>Results</h4>A total of","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-05T21:42:46.076Z","creation":"2026-05-22T03:15:37.588Z"},"accession":"S-EPMC12664166","cross_references":{"pubmed":["41318659"],"doi":["10.1186/s42234-025-00191-8"]}}