Precision ICU Resource Planning: A Multimodal Model for Brain Surgery Outcomes
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
Although advances in brain surgery techniques have led to fewer postoperative
complications requiring Intensive Care Unit (ICU) monitoring, the routine
transfer of patients to the ICU remains the clinical standard, despite its high
cost. Predictive Gradient Boosted Trees based on clinical data have attempted
to optimize ICU admission by identifying key risk factors pre-operatively;
however, these approaches overlook valuable imaging data that could enhance
prediction accuracy. In this work, we show that multimodal approaches that
combine clinical data with imaging data outperform the current clinical data
only baseline from 0.29 [F1] to 0.30 [F1], when only pre-operative clinical
data is used and from 0.37 [F1] to 0.41 [F1], for pre- and post-operative data.
This study demonstrates that effective ICU admission prediction benefits from
multimodal data fusion, especially in contexts of severe class imbalance.
