发表期刊：

Information Sciences

论文层级：

人大A,中科院一区,JCR一区

论文作者：

Minghui Qian, Mengchun Zhao, Meng Pan, Yuchen Pan（Corresponding author）, Desheng Wu, David L. Olson , Weiping Ding

论文摘要：

To address the challenge of efficiently allocating limited medical resources in China, this study proposes a similarity-driven online doctor recommendation model (SimRec) to improve healthcare accessibility and resource utilization. The model was developed using object-oriented methods to analyze the current service mode of online consultation platforms, incorporating the actual needs of doctors and patients into its design. The framework consists of two layers: the object layer, which represents patient and doctor models abstractly, and the function layer, which implements recommendation technology. The function layer divides the process into two stages—department prediction and doctor-patient matching—to guide patients to appropriate departments, recommend suitable doctors, and allocate doctors based on patient needs. Tests on real-world data demonstrate that SimRec achieves better performance compared to baseline models in both department prediction and doctor-patient matching, indicating its effectiveness in optimizing medical resource allocation.