An Integration of Decision Tree and Visual Analysis to Analyze Intracranial Pressure

Abstract

In Traumatic Brain Injury (TBI), elevated Intracranial Pressure (ICP) causes severe brain damages due to hemorrhage and swelling. Monitoring ICP plays an important role in the treatment of TBI patients because ICP is considered a strong predictor of neurological outcome and a potentially amenable method to treat patients. However, it is difficult to predict and measure accurate ICP due to the complex nature of patients' clinical conditions. ICP monitoring for severe TBI patient is a challenging problem for clinicians because traditionally known ICP monitoring is an invasive procedure by placing a device inside the brain to measure pressure. Therefore, ICP monitoring might have a high infection risk and cause medical complications. In here, an ICP monitoring using texture features is proposed to overcome this limitation. The combination of image processing methods and a decision tree algorithm is utilized to estimate ICP of TBI patients noninvasively. In addition, a visual analytics tool is used to conduct an interactive visual factor analysis and outlier detection.

Explaining artificial intelligence with visual analytics in healthcare

“…Results from k-means and k-nearest neighbors are usually visualized in scatter plots with dots colored according to their cluster (e.g., Klemm et al, 2014;X. Ji, Shen, et al, 2019, Figure 2a), or in projection plots after dimension reduction (e.g., R. Guo et al, 2020;S.-Y. Ji et al, 2017).…”