P-029 Interpretable machine learning modeling for thrombectomy outcome prediction in ischemic stroke

ConclusionMachine learning applied to quantifiable image features from CT and CTA alongside basic clinical characteristics constitutes a promising automated method for predicting stroke prognosis. Our interpretable model allows understanding of which features contribute the most to post-thrombectomy outcome prediction and which feature values the model used to individually predict each patient outcome.Abstract P-029 Figure 1Disclosures M. Jabal: None. O. Joly: 5; C; Brainomix. D. Kallmes: None. G. Harston: 4; C; Brainomix. 5; C; Brainomix. A. Rabinstein: None. T. Huynh: None. W. Brinjikji: None.

Source: Journal of NeuroInterventional Surgery - July 23, 2022 Category: Neurosurgery Authors: Jabal, M., Joly, O., Kallmes, D., Harston, G., Rabinstein, A., Huynh, T., Brinjikji, W. Tags: SNIS 19th annual meeting oral poster abstracts Source Type: research