Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion

• URL: http://arxiv.org/abs/2510.26444v1
• Date: Thu, 30 Oct 2025 12:50:12 GMT
• Title: Personalized Treatment Outcome Prediction from Scarce Data via Dual-Channel Knowledge Distillation and Adaptive Fusion
• Authors: Wenjie Chen, Li Zhuang, Ziying Luo, Yu Liu, Jiahao Wu, Shengcai Liu,
• Abstract summary: We propose a cross-fidelity knowledge distillation and adaptive fusion network (CFKD-AFN) to enhance predictions on scarce but high-fidelity trial data.<n>Experiments on treatment outcome prediction for the chronic obstructive pulmonary disease demonstrate significant improvements of CFKD-AFN over state-of-the-art methods.<n>We extend CFKD-AFN to an interpretable variant, enabling the exploration of latent medical semantics to support clinical decision-making.
• Score: 11.196642331173862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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