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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, Seyoung; Chang, Won Hyuk; Kim, Deog Young; Lee, Jongmin; Sohn, Min Kyun; Song, Min-Keun; Shin, Yong-Il; Lee, Yang-Soo; Joo, Min Cheol; Lee, So Young; Han, Junhee; Ahn, Jeonghoon; Oh, Gyung-Jae; Kim, Young-Taek; Kim, Kwangsu; Kim, Yun-Hee
- Ewha Authors
- 안정훈
- SCOPUS Author ID
- 안정훈
- Issue Date
- 2023
- Journal Title
- FRONTIERS IN NEUROLOGY
- ISSN
- 1664-2295
- Citation
- FRONTIERS IN NEUROLOGY vol. 14
- Keywords
- stroke; functional recovery; artificial intelligence; machine learning; clustering; prediction
- Publisher
- FRONTIERS MEDIA SA
- Indexed
- SCIE; 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
- Appears in Collections:
- 신산업융합대학 > 융합보건학과 > Journal papers
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