biostudies-literatureUnknown12(1)Kim JTClustering stroke patients with similar characteristics to predict subsequent vascular outcome events is critical. This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering method with a maximally distinct 1-year outcome in patients with ischemic stroke. Prospective stroke registry data from a comprehensive stroke center from January 2011 to July 2018 were retrospectively analyzed. Patients with acute ischemic stroke within 7 days of onset were included. The primary outcomes were the composite of all strokes (either hemorrhagic or ischemic), myocardial infarction, and all-cause mortality within one year. Neural network-based clustering models (deep lifetime clustering) were compared with other clustering models Scientific reports9420https://www.ebi.ac.uk/biostudies/studies/S-EPMC9177616biostudies-literatureNeural network-based clustering model of ischemic stroke patients with a maximally distinct distribution of 1-year vascular outcomes.PMC9177616Kim NRKim MSChoi SHLee SHKim BCOh SChoi JPark MSKim JTfalseNeural network-based clustering model of ischemic stroke patients with a maximally distinct distribution of 1-year vascular outcomes.Clustering stroke patients with similar characteristics to predict subsequent vascular outcome events is critical. This study aimed to compare several clustering methods, particularly a deep neural network-based model, and identify the best clustering method with a maximally distinct 1-year outcome in patients with ischemic stroke. Prospective stroke registry data from a comprehensive stroke center from January 2011 to July 2018 were retrospectively analyzed. Patients with acute ischemic stroke within 7 days of onset were included. The primary outcomes were the composite of all strokes (either hemorrhagic or ischemic), myocardial infarction, and all-cause mortality within one year. Neural network-based clustering models (deep lifetime clustering) were compared with other clustering models 2022-01-01T00:00:00Z2022 Jun2025-04-05T15:05:56.933Z2024-12-04T13:10:35.009ZS-EPMC91776163567641310.1038/s41598-022-13636-w