Predictors of Outcome Clusters in Patients With Unruptured Intracranial Aneurysms Treated With Microsurgery: An Unsupervised Machine Learning Analysis
Unsupervised machine learning grouped outcomes into 3 distinct, clinically meaningful clusters. Vascular comorbidities, rather than aneurysm characteristics, significantly predicted attribution to outcome clusters.
Authors
- Anant Naik (ORCID: https://orcid.org/0000-0002-7491-5401)
- Joshua S. Catapano (ORCID: https://orcid.org/0000-0001-6599-0269)
- Olga Ciobanu-Caraus (ORCID: https://orcid.org/0000-0002-0082-2026)
- Visish Mani Srinivasan (ORCID: https://orcid.org/0000-0001-7673-6805)
- Lea Scherschinski (ORCID: https://orcid.org/0000-0002-2791-3809)
- Ashia M. Hackett
- Emmajane G. Rhodenhiser
- Adam T. Eberle (ORCID: https://orcid.org/0009-0003-3905-7338)
- Joelle N. Hartke
- E Winkler
- Michael T. Lawton
- Elsa Nico
Institutions
- Barrow Neurological Institute (US)
- St. Joseph's Hospital and Medical Center (US)
Publication Details
- Journal
- Neurosurgery Open
- Published
- 2026-01-08
- DOI
- https://doi.org/10.1227/neuprac.0000000000000193
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00