Neural network-based clustering model of ischemic stroke patients with a maximally distinct distribution of 1-year vascular outcomes
Joon‐Tae Kim, Nu Ri Kim, Su Hoon Choi, Seung‐Won Oh, Man‐Seok Park, Seung‐Han Lee, Byeong C. Kim, Jonghyun Choi, Min Soo Kim
Abstract
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 (k-prototype and semi-supervised clustering, SSC) and a conventional risk score (Stroke Prognostic Instrument-II, SPI-II) to obtain a distinct distribution of 1-year vascular events. Ultimately, 7,650 patients were included, and the 1-year primary outcome event occurred in 13.1%. The DLC-Kuiper UB model had a significantly higher C-index (0.674), log-rank score (153.1), and Brier score (0.08) than the other cluster models (SSC and DLC-MMD) and the SPI-II score. There were significant differences in primary outcome events among the 3 clusters (41.7%, 13.4%, and 6.5% in clusters 0, 1, and 2, respectively) when the DLC-Kuiper UB model was used. A neural network-based clustering model, the DLC-Kuiper UB model, can improve the clustering of stroke patients with a maximally distinct distribution of 1-year vascular outcomes among each cluster. Further studies are warranted to validate this deep neural network-based clustering model in ischemic stroke.