{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["19(10)"],"submitter":["Ghosh S"],"funding":["University of Oklahoma"],"pubmed_abstract":["This study aims to develop explainable AI methods for matching patients with phase 1 oncology clinical trials using Natural Language Processing (NLP) techniques to address challenges in patient recruitment for improved efficiency in drug development. A prototype system based on modern NLP techniques has been developed to match patient records with phase 1 oncology clinical trial protocols. Four criteria are considered for the matching: cancer type, performance status, genetic mutation, and measurable disease. The system outputs a summary matching score along with explanations of the evidence. The outputs of the AI system were evaluated against the ground truth matching results provided by the domain expert on a dataset of twelve synthesized dummy patient records and six clinical trial prot"],"journal":["PloS one"],"pagination":["e0311510"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11500892"],"repository":["biostudies-literature"],"pubmed_title":["Harnessing explainable artificial intelligence for patient-to-clinical-trial matching: A proof-of-concept pilot study using phase I oncology trials."],"pmcid":["PMC11500892"],"pubmed_authors":["Lu K","Ghosh S","Ganesan A","Pan C","Abushukair HM","Naqash AR"],"additional_accession":[]},"is_claimable":false,"name":"Harnessing explainable artificial intelligence for patient-to-clinical-trial matching: A proof-of-concept pilot study using phase I oncology trials.","description":"This study aims to develop explainable AI methods for matching patients with phase 1 oncology clinical trials using Natural Language Processing (NLP) techniques to address challenges in patient recruitment for improved efficiency in drug development. A prototype system based on modern NLP techniques has been developed to match patient records with phase 1 oncology clinical trial protocols. Four criteria are considered for the matching: cancer type, performance status, genetic mutation, and measurable disease. The system outputs a summary matching score along with explanations of the evidence. The outputs of the AI system were evaluated against the ground truth matching results provided by the domain expert on a dataset of twelve synthesized dummy patient records and six clinical trial prot","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2025-04-04T03:04:26.688Z","creation":"2025-04-04T03:04:26.688Z"},"accession":"S-EPMC11500892","cross_references":{"pubmed":["39446771"],"doi":["10.1371/journal.pone.0311510"]}}