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.

Publikation

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.

Wissenschaftlicher Artikel/Review - 04.07.2024

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

Zitation

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.

Art

Wissenschaftlicher Artikel/Review (Englisch)

Zeitschrift

EBioMedicine 2024; 106

Veröffentlichungsdatum

04.07.2024

eISSN (Online)

2352-3964

Seiten

105226

Kurzbeschreibung/Zielsetzung

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.