Paper Type
Short
Paper Number
PACIS2026-1789
Description
Online health consultation faces challenges of physician scarcity and potential prescription bias. This study proposes a Role-Aware Debiased Curriculum Learning (RADCL) framework for dialogue-based medication recommendation to address the limitation of traditional curriculum learning in distinguishing heterogeneous sources of sample difficulty arising from doctor-side diagnostic complexity and patient-side symptom ambiguity. Following the design science methodology, RADCL integrates three modules: a dialogue encoding module with BERT and Graph Attention Networks for structural understanding, a role-aware difficulty measurement module introducing attention-based metrics to quantify doctor and patient contributions, and an iterative curriculum learning module dynamically adjusting sample weights. Experiments on the DIALMED dataset with five-fold cross-validation demonstrate that RADCL outperforms state-of-the-art baselines, achieving 4.04% and 2.91% relative improvements in Jaccard and F1 scores, respectively. This work advances curriculum learning in healthcare NLP and provides a robust solution for online medication consultation.
Recommended Citation
Kuang, Junwei; Liu, Yi; Lian, Yunzeng; Zhang, Yuxin; Zhang, Xinyu; and Zhao, Shiyi, "A Role-Aware Debiased Curriculum Learning Approach for Dialogue-based Medication Recommendation" (2026). PACIS 2026 Proceedings. 18.
https://aisel.aisnet.org/pacis2026/ishealthcare/ishealthcare/18
A Role-Aware Debiased Curriculum Learning Approach for Dialogue-based Medication Recommendation
Online health consultation faces challenges of physician scarcity and potential prescription bias. This study proposes a Role-Aware Debiased Curriculum Learning (RADCL) framework for dialogue-based medication recommendation to address the limitation of traditional curriculum learning in distinguishing heterogeneous sources of sample difficulty arising from doctor-side diagnostic complexity and patient-side symptom ambiguity. Following the design science methodology, RADCL integrates three modules: a dialogue encoding module with BERT and Graph Attention Networks for structural understanding, a role-aware difficulty measurement module introducing attention-based metrics to quantify doctor and patient contributions, and an iterative curriculum learning module dynamically adjusting sample weights. Experiments on the DIALMED dataset with five-fold cross-validation demonstrate that RADCL outperforms state-of-the-art baselines, achieving 4.04% and 2.91% relative improvements in Jaccard and F1 scores, respectively. This work advances curriculum learning in healthcare NLP and provides a robust solution for online medication consultation.
Comments
14-Healthcare