Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning

Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning

AbstractObjective The identification of spinal tuberculosis subphenotypes is an integral component of precision medicine. However, we lack proper study models to identify subphenotypes in patients with spinal tuberculosis. Here we identified possible subphenotypes of spinal tuberculosis and compared...

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Bibliographic Details
Main Authors: Yuanlin Yao, Shaofeng Wu, Chong Liu, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Chengqian Huang, Sitan Feng, Bin Zhang, Siling Wu, Fengzhi Ma, Lu Liu, Xinli Zhan
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:Annals of Medicine
Subjects:
Spinal tuberculosis
machine learning
cluster analysis
K-means
heterogeneity
Online Access:https://www.tandfonline.com/doi/10.1080/07853890.2023.2249004

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https://www.tandfonline.com/doi/10.1080/07853890.2023.2249004

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