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Clustering and prediction of long-term functional recovery patterns in first-time stroke patients

Title
Clustering and prediction of long-term functional recovery patterns in first-time stroke patients
Authors
Shin, SeyoungChang, Won HyukKim, Deog YoungLee, JongminSohn, Min KyunSong, Min-KeunShin, Yong-IlLee, Yang-SooJoo, Min CheolLee, So YoungHan, JunheeAhn, JeonghoonOh, Gyung-JaeKim, Young-TaekKim, KwangsuKim, Yun-Hee
Ewha Authors
안정훈
SCOPUS Author ID
안정훈scopus
Issue Date
2023
Journal Title
FRONTIERS IN NEUROLOGY
ISSN
1664-2295JCR Link
Citation
FRONTIERS IN NEUROLOGY vol. 14
Keywords
strokefunctional recoveryartificial intelligencemachine learningclusteringprediction
Publisher
FRONTIERS MEDIA SA
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
ObjectivesThe purpose of this study was to cluster long-term multifaceted functional recovery patterns and to establish prediction models for functional outcome in first-time stroke patients using unsupervised machine learning. MethodsThis study is an interim analysis of the dataset from the Korean Stroke Cohort for Functioning and Rehabilitation (KOSCO), a long-term, prospective, multicenter cohort study of first-time stroke patients. The KOSCO screened 10,636 first-time stroke patients admitted to nine representative hospitals in Korea during a three-year recruitment period, and 7,858 patients agreed to enroll. Early clinical and demographic features of stroke patients and six multifaceted functional assessment scores measured from 7 days to 24 months after stroke onset were used as input variables. K-means clustering analysis was performed, and prediction models were generated and validated using machine learning. ResultsA total of 5,534 stroke patients (4,388 ischemic and 1,146 hemorrhagic; mean age 63 center dot 31 +/- 12 center dot 86; 3,253 [58.78%] male) completed functional assessments 24 months after stroke onset. Through K-means clustering, ischemic stroke (IS) patients were clustered into five groups and hemorrhagic stroke (HS) patients into four groups. Each cluster had distinct clinical characteristics and functional recovery patterns. The final prediction models for IS and HS patients achieved relatively high prediction accuracies of 0.926 and 0.887, respectively. ConclusionsThe longitudinal, multi-dimensional, functional assessment data of first-time stroke patients were successfully clustered, and the prediction models showed relatively good accuracies. Early identification and prediction of long-term functional outcomes will help clinicians develop customized treatment strategies.
DOI
10.3389/fneur.2023.1130236|http://dx.doi.org/10.3389/fneur.2023.1130236
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신산업융합대학 > 융합보건학과 > Journal papers
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