Wei Jianhua, Yan Honglin, Shao Xiaoli, Zhao Lili, Han Lin, Yan Peng, Wang Shengyu
The Bidding Procurement Office, The First Affiliated Hospital of Xi'an Medical University, Xian, China.
Department of Gastroenterology, The First Affiliated Hospital of Xi'an Medical University, Xian, China.
PeerJ Comput Sci. 2024 Feb 20;10:e1880. doi: 10.7717/peerj-cs.1880. eCollection 2024.
This article presents a hybrid recommender framework for smart medical systems by introducing two methods to improve service level evaluations and doctor recommendations for patients. The first method uses big data techniques and deep learning algorithms to develop a registration review system in medical institutions. This system outperforms conventional evaluation methods, thus achieving higher accuracy. The second method implements the term frequency and inverse document frequency (TF-IDF) algorithm to construct a model based on the patient's symptom vector space, incorporating score weighting, modified cosine similarity, and K-means clustering. Then, the alternating least squares (ALS) matrix decomposition and user collaborative filtering algorithm are applied to calculate patients' predicted scores for doctors and recommend top-performing doctors. Experimental results show significant improvements in metrics called precision and recall rates compared to conventional methods, making the proposed approach a practical solution for department triage and doctor recommendation in medical appointment platforms.
本文通过引入两种方法来改进智能医疗系统的服务水平评估和为患者推荐医生，提出了一种混合推荐框架。第一种方法使用大数据技术和深度学习算法来开发医疗机构的挂号审核系统。该系统优于传统评估方法，从而实现更高的准确性。第二种方法实施词频-逆文档频率（TF-IDF）算法，基于患者的症状向量空间构建模型，纳入分数加权、修正余弦相似度和K均值聚类。然后，应用交替最小二乘法（ALS）矩阵分解和用户协同过滤算法来计算患者对医生的预测分数，并推荐表现最佳的医生。实验结果表明，与传统方法相比，在称为精确率和召回率的指标上有显著提高，使得所提出的方法成为医疗预约平台中科室分诊和医生推荐的实用解决方案。
