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.

翻译：尽管脑外科手术技术的进步已显著降低术后并发症发生率，从而减少了对重症监护病房（ICU）监测的需求，但临床上仍普遍采用术后常规转入ICU的标准做法，尽管其成本高昂。基于临床数据的预测性梯度提升树模型曾尝试通过术前识别关键风险因素来优化ICU收治决策；然而，这些方法忽略了可提升预测准确性的宝贵影像数据。本研究表明，融合临床数据与影像数据的多模态方法，在仅使用术前临床数据时，其性能从基线0.29 [F1]提升至0.30 [F1]；而在结合术前与术后数据时，性能从0.37 [F1]提升至0.41 [F1]。此项研究证实，有效的ICU收治预测得益于多模态数据融合，尤其在面临严重类别不平衡的临床场景中。