量子神经网络提升结肠癌吻合口漏预测灵敏度，优于传统模型。
Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
- 用量子特征编码构建量子神经网络，模拟噪声下训练
- 量子模型敏感度达83.3%，高于经典模型的66.7%
- 适合关注低发生率临床风险预测的医疗AI研究者
本研究评估结直肠癌风险因素，并比较经典模型与量子神经网络（QNN）在吻合口漏预测中的表现。基于14%漏发生率的临床数据，采用ZZFeatureMap编码与RealAmplitudes、EfficientSU2变分形式，在模拟噪声条件下进行测试。经$F_β$优化的量子配置显著提升敏感度至83.3%，优于经典基线的66.7%。结果表明，量子特征空间更擅长识别少数类，对低发生率临床风险预测具有重要意义。研究还探索了多种优化器在噪声环境下的表现，揭示硬件部署的关键权衡与未来方向。
原文摘要 · Abstract (English)
This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analyzing clinical data with 14\% leak prevalence, we tested ZZFeatureMap encodings with RealAmplitudes and EfficientSU2 ansatze under simulated noise. $F_β$-optimized quantum configurations yielded significantly higher sensitivity (83.3\%) than classical baselines (66.7\%). This demonstrates that quantum feature spaces better prioritize minority class identification, which is critical for low-prevalence clinical risk prediction. Our work explores various optimizers under noisy conditions, highlighting key trade-offs and future directions for hardware deployment.
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