Artificial-intelligent prediction model of occurrence of cerebral vasospasms based on machine-learning.

Abstract

Background and Study Aims Symptomatic cerebral vasospasms are deleterious complication of the rupture of a cerebral aneurysm and potentially lethal. The existing scales used to classify the initial presentation of a subarachnoid haemorrhage (SAH), offer a blink of the outcome and the possibility of occurrence of symptomatic cerebral vasospasms. Altogether, they are neither sufficient to predict outcome or occurrence of events reliably nor do they offer a unite front. This study tests the common grading scales and factors, that otherwise effect the outcome, in an artificial-intelligent based algorithm in order to create a reliable prediction model for the occurrence of cerebral vasospasms. Material and Methods Applying the R environment an easy to operate command line was programmed to prognosticate the occurrence of vasospasms. Eighty-seven patients with aneurysmal SAH during a 24 months-period of time were included for study purposes. The Holdout and the Cross-Validation methods were used to evaluate the algorithm (65 patients validation set. 22 patients test set). The Support Vector Machines (ksvm) classification method provided a high accuracy. The medical data set included demographic data, the Hunt & Hess scale, Fisher grade, BNI scale, length of intervention for aneurysmal repair, etc. Results Our prediction model based on the AI algorithm demonstrated an accuracy of 61%-86% for the event of symptomatic vasospasms. For the subgroup analysis, patients of the surgical cohort 28,8% (n=13) developed symptomatic vasospasm, whereas admitted with Fisher scale grade 4 50% (n=7), with H&H 5 37,5% (n=5) and 28,5% (n=4) with BNI 5. Respectively, in the endovascular cohort vasospasms occurred in 31,8% (n=14) patients, with Fisher grade 4 69% (n=9), H&H 5 23% (n=3) and 7% (n=1) with BNI 5. Conclusion From our data, we may believe that the algorithm presented can help in identifying patients with SAH being at 'high' or 'low risk' for the development of symptomatic vasospasms. This risk balancing might further allow the treating physician to go for an early intervention trying to prevent permanent sequelae. Certainly, accuracy will improve with a higher caseload and more statistical coefficients.