Precision ICU Resource Planning: A Multimodal Model for Brain Surgery Outcomes

• URL: http://arxiv.org/abs/2412.15818v1
• Date: Fri, 20 Dec 2024 11:59:34 GMT
• Title: Precision ICU Resource Planning: A Multimodal Model for Brain Surgery Outcomes
• Authors: Maximilian Fischer, Florian M. Hauptmann, Robin Peretzke, Paul Naser, Peter Neher, Jan-Oliver Neumann, Klaus Maier-Hein,
• Abstract summary: multimodal approaches that combine clinical data with imaging data outperform the current clinical data only baseline from 0.29 [F1] to 0.30 [F1] This study demonstrates that effective ICU admission prediction benefits from multimodal data fusion, especially in contexts of severe class imbalance.
• Score: 0.0918307006755572
• License:
• 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.

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