P-008 Probabilistic ensemble learning for prediction of stroke thrombectomy outcomes from the neurovascular quality initiative-quality outcomes database (NVQI-QOD) registry

ConclusionsThis study demonstrates the utility of probabilistic ensemble learning in clinical decision-making and prognosis. It can provide robust predictions as well as quantify data uncertainty. Our results regarding NIHSS changes reinforce the substantial benefits of MT, that can improve outcomes in nearly half of patients. The degree of disability relevant to the 90-day follow-up mRS can be determined by probabilistic learning available as early as discharge.Disclosures C. Zhou: None. S. Faruqui: None. A. Patel: None. R. Abdalla: None. A. Shaibani: None. M. Potts: None. B. Jahromi: None. S. Ansari: None. D. Cantrell: None.

Source: Journal of NeuroInterventional Surgery - July 30, 2023 Category: Neurosurgery Authors: Zhou, C., Faruqui, S., Patel, A., Abdalla, R., Shaibani, A., Potts, M., Jahromi, B., Ansari, S., Cantrell, D. Tags: SNIS 20th annual meeting oral poster abstracts Source Type: research