PREDICTION OF INTRACRANIAL PRESSURE IN TRAUMATIC BRAIN INJURY PATIENTS

Abstract

The serious neurological consequences and increased intracranial pressure (ICP) that may result from traumatic brain injury (TBI) make it a major issue in public health. Improving patient outcomes and facilitating rapid intervention requires accurate ICP prediction in TBI patients. Our work suggests a method for predicting ICP in individuals with traumatic brain injuries (TBI) using machine learning and Random Forest regression. We gathered a large dataset from TBI patients that included demographic data, physiological measures, and clinical notes. We used feature selection approaches to identify important characteristics for predicting ICP after preprocessing the data to manage outliers and missing values. Afterwards, the dataset was used to train a Random Forest regression model. This approach captured complicated correlations between variables and ICP by leveraging an ensemble of decision trees. The model's capacity to reliably predict ICP was supported by evaluation measures that showed promise, such as low Mean Absolute Error (MAE) and Mean Squared Error (MSE). The most important characteristics contributing to ICP prediction were revealed via model interpretation. Our results indicate that the Random Forest regression model might be useful in clinical settings for predicting ICP in patients with traumatic brain injuries. This could lead to earlier intervention and more individualized approaches to patient care. To evaluate the model's practicality and effect on patient outcomes, more validation and clinical deployment are necessary.