医联体模式下双向转诊中的医患匹配模型研究

    Research on the Doctor-Patient Matching Model in Two-way Referral under the Medical Alliance Model

    • 摘要: 为精准匹配医患需求、优化资源配置,构建了一个适用于医联体模式下双向转诊中的医患配对模型。基于冰山理论构建了医生评价框架,并结合考虑上下转诊的评价指标,得到双向转诊的医生评价模型;利用支持向量机算法实现患者疾病类型预测,并对风险因素挖掘后采用随机森林算法得到患者评价模型,从而构建了转诊阶段医生胜任力模型和患者人物特征画像的完整评价体系。基于Gale-Shapley算法根据患者患病风险进行首轮医患配对,并结合个性化排序满足患者要求以提升配对满意度,通过动态多目标优化算法实现患者的精准匹配。通过使用好大夫网站和线下医院真实数据进行数值实验,验证了该模型可行性与精确度。

       

      Abstract: To precisely match medical needs and optimize resource allocation, a doctor-patient matching model in two-way referral under the medical alliance model has been established. Firstly, the doctor evaluation framework was constructed based on the iceberg theory, and the doctor evaluation model of bidirectional referral was obtained by combining the evaluation indexes considered by up-and-down referral. The support vector machine algorithm was used to predict the patient's disease type, and the random forest algorithm was used to obtain the patient evaluation model after mining the risk factors, thus constructing a complete evaluation system for the doctor competency model and patient characteristic profile at the referral stage. Secondly, the first round of doctor-patient matching was conducted based on the Gale-Shapley algorithm, incorporating the patients' disease risks. Additionally, by integrating personalized rankings, patients′ requirements were fulfilled to enhance matching satisfaction. Ultimately, the dynamic multi-objective optimization algorithm Was employed to achieve precise matching for all patients. Finally, the feasibility and accuracy of the model were verified by using the real data of Haodaifu website and offline hospitals for numerical experiments.

       

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