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

Wenjie Chen, Li Zhuang, Ziying Luo, Yu Liu, Jiahao Wu, Shengcai Liu

TL;DR

The paper tackles PTOP when high-fidelity trial data are scarce by proposing CFKD-AFN, a cross-fidelity framework that leverages abundant low-fidelity simulations through dual-channel knowledge distillation (macroscopic predictions and microscopic features) and an attention-guided fusion with high-fidelity inputs. It demonstrates substantial improvements in COPD treatment outcome prediction over state-of-the-art baselines and shows robustness to very small high-fidelity datasets. An interpretable variant, iCFKD-AFN, uses mutual-information disentanglement to reveal latent medical semantics, supporting clinical decision-making. Together, CFKD-AFN and its interpretable extension advance precision medicine by enabling accurate, explainable predictions in data-scarce settings and informing treatment choices for rare patient groups.

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.

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

TL;DR

The paper tackles PTOP when high-fidelity trial data are scarce by proposing CFKD-AFN, a cross-fidelity framework that leverages abundant low-fidelity simulations through dual-channel knowledge distillation (macroscopic predictions and microscopic features) and an attention-guided fusion with high-fidelity inputs. It demonstrates substantial improvements in COPD treatment outcome prediction over state-of-the-art baselines and shows robustness to very small high-fidelity datasets. An interpretable variant, iCFKD-AFN, uses mutual-information disentanglement to reveal latent medical semantics, supporting clinical decision-making. Together, CFKD-AFN and its interpretable extension advance precision medicine by enabling accurate, explainable predictions in data-scarce settings and informing treatment choices for rare patient groups.

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.

Paper Structure

This paper contains 28 sections, 2 equations, 5 figures, 12 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Review of PTOP
  4. Review of Multi-fidelity Transfer Learning
  5. Review of Multi-fidelity Transfer Learning
  6. Problem Formulation and Proposed Method
  7. Problem Formulation
  8. The Proposed CFKD-AFN Method
  9. Traditional Algorithms
  10. CFKD-AFN
  11. Experimental Study on Treatment Outcome Prediction for COPD
  12. Application Background of COPD
  13. Experimental Setting
  14. Compared Methods
  15. Performance Metric
  16. ...and 13 more sections

Figures (5)

  • Figure 1: Illustration of PTOP (RSHD vs. PSTD), the challenge of scarce high-fidelity data in PSTD, and the idea of using simulation to tackle this challenge.
  • Figure 2: The architectures of the pretraining-fine-tuning and the multi-level data fusion methods.
  • Figure 3: The architecture of CFKD-AFN.
  • Figure 4: The architecture of iCFKD-AFN.
  • Figure 5: Contributions of the input features on the decoupled vectors with different amounts of high-fidelity data.

Theorems & Definitions (1)

  • Definition 1