融合影像与临床数据，提升脑手术后重症监护入院预测准确率
Precision ICU Resource Planning: A Multimodal Model for Brain Surgery Outcomes
- 结合术前临床数据与影像信息进行多模态融合预测
- 使用术前术后数据时F1得分提升至0.41，优于仅用临床数据的0.37
- 对重症监护资源紧张场景具有实用价值，适合医疗规划研究者
尽管脑外科技术进步减少了术后需转入重症监护室（ICU）的并发症，但常规仍将患者转入ICU，成本高昂。基于临床数据的预测梯度提升树虽尝试优化入院决策，却忽视了潜在的影像数据价值。本研究证明，融合临床与影像数据的多模态方法显著优于仅使用临床数据的基准模型：仅用术前数据时F1从0.29提升至0.30；使用术前及术后数据时，F1从0.37提升至0.41。结果表明，在严重类别不平衡背景下，多模态数据融合能有效提升ICU入院预测性能。
原文摘要 · Abstract (English)
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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