To explore mortality of posterior fossa hemorrhage with artificial intelligence approach

Intracranial hemorrhage refers to bleeding that occurs within the skull. Several factors have been attributed to the mortality of posterior fossa hemorrhage, including clinical presentations such as Glascow coma scale, obstructive hydrocephalus, imaging features such as hematoma size, intraventricular hemorrhage, brain stem compression, and combined factor such as ICH score. First, this study attempts to identify the most influential factor combinations to explore mortality of posterior fossa hemorrhage. We will adopt appropriate algorithms to cluster the data so that the patient data with similar features are appropriately clustered. And we will build a corresponding prediction model for each cluster data to construct a multiple prediction classifier.