Predicting the trajectory of intracranial pressure in patients with traumatic brain injury: evaluation of a foundation model for time series

TL;DR

This study explores using foundation models with transfer learning to predict intracranial pressure trajectories in traumatic brain injury patients, aiming to improve early detection of dangerous pressure increases despite limited data.

Contribution

It introduces a novel application of foundation models for time series prediction in TBI-related ICP, addressing data scarcity challenges.

Findings

Foundation models show potential in ICP trajectory prediction.

Transfer learning improves model performance with limited data.

Early warning capability could enhance clinical intervention.

Abstract

Patients with traumatic brain injury (TBI) often experience pathological increases in intracranial pressure (ICP), leading to intracranial hypertension (tIH), a common and serious complication. Early warning of an impending rise in ICP could potentially improve patient outcomes by enabling preemptive clinical intervention. However, the limited availability of patient data poses a challenge in developing reliable prediction models. In this study, we aim to determine whether foundation models, which leverage transfer learning, may offer a promising solution.