Machine learning-based cluster analysis identifies four unique phenotypes of patients with degenerative cervical myelopathy with distinct clinical profiles and long-term functional and neurological outcomes.

Publication

Machine learning-based cluster analysis identifies four unique phenotypes of patients with degenerative cervical myelopathy with distinct clinical profiles and long-term functional and neurological outcomes.

Journal Paper/Review - Jul 4, 2024

Pedro Karlo M, Alvi MohamAli, Hejrati Nader, Quddusi Ayesha, Singh Anoushka, Fehlings Michael G

Citation

Pedro K, Alvi M, Hejrati N, Quddusi A, Singh A, Fehlings M. Machine learning-based cluster analysis identifies four unique phenotypes of patients with degenerative cervical myelopathy with distinct clinical profiles and long-term functional and neurological outcomes. EBioMedicine 2024; 106:105226.

Type

Journal Paper/Review (English)

Journal

EBioMedicine 2024; 106

Publication Date

Jul 4, 2024

Issn Electronic

2352-3964

Pages

105226

Brief description/objective

Degenerative cervical myelopathy (DCM), the predominant cause of spinal cord dysfunction among adults, exhibits diverse interrelated symptoms and significant heterogeneity in clinical presentation. This study sought to use machine learning-based clustering algorithms to identify distinct patient clinical profiles and functional trajectories following surgical intervention.