This project was developed during a hackathon focused on applying artificial intelligence to healthcare challenges. The goal was to build a machine learning model capable of processing and predicting intracranial pressure (ICP) data from patient monitoring systems.
Intracranial pressure monitoring is critical in neurology and neurosurgery — elevated ICP can indicate life-threatening conditions such as traumatic brain injury, hydrocephalus, or intracranial hemorrhage. Traditional monitoring requires invasive procedures and continuous manual observation by medical staff. The idea behind this project was to explore whether AI can assist in early detection of dangerous pressure trends.
The model was trained on time-series ICP data and uses signal processing techniques to clean and normalize the raw sensor readings. Feature engineering was applied to extract meaningful patterns from the pressure waveforms, including mean pressure values, pulse amplitude, and trend indicators over sliding time windows.
The prediction pipeline uses a combination of data preprocessing, feature extraction, and a machine learning model to forecast short-term ICP trends. This could potentially help medical professionals identify dangerous pressure elevations before they become critical, giving them more time to intervene.
The project demonstrated the feasibility of using AI for ICP trend prediction and received positive feedback from the hackathon judges for its practical healthcare application.