Personalized treatment outcome prediction based on trial data for small-sample and rare patient groups is critical in precision medicine. However, the costly trial data limit the prediction performance. To address this issue, we propose a cross-fidelity knowledge distillation and adaptive fusion network (CFKD-AFN), which leverages abundant but low-fidelity simulation data to enhance predictions on scarce but high-fidelity trial data. CFKD-AFN incorporates a dual-channel knowledge distillation module to extract complementary knowledge from the low-fidelity model, along with an attention-guided fusion module to dynamically integrate multi-source information. Experiments on treatment outcome prediction for the chronic obstructive pulmonary disease demonstrates significant improvements of CFKD-AFN over state-of-the-art methods in prediction accuracy, ranging from 6.67\% to 74.55\%, and strong robustness to varying high-fidelity dataset sizes. Furthermore, we extend CFKD-AFN to an interpretable variant, enabling the exploration of latent medical semantics to support clinical decision-making.

翻译：针对小样本和罕见患者群体的试验数据进行个性化治疗结果预测，在精准医学中至关重要。然而，昂贵的试验数据限制了预测性能。为解决这一问题，我们提出了一种跨保真度知识蒸馏与自适应融合网络（CFKD-AFN），该网络利用丰富但保真度较低的模拟数据来增强对稀缺但保真度较高的试验数据的预测。CFKD-AFN包含一个双通道知识蒸馏模块，用于从低保真度模型中提取互补知识，以及一个注意力引导的融合模块，用于动态整合多源信息。在慢性阻塞性肺疾病治疗结果预测上的实验表明，CFKD-AFN在预测准确性上相比现有最先进方法有显著提升，提升幅度从6.67%到74.55%，且对不同规模的高保真度数据集展现出强鲁棒性。此外，我们将CFKD-AFN扩展为可解释的变体，使其能够探索潜在的医学语义，以支持临床决策。