摘要
针对智慧医院缺乏有效的智能辅助诊疗应用问题,提出一种改进聚类算法来设计与实践智慧医院的相关医疗应用,实现诊疗辅助。该算法结合一种改进的遗传算法和网络中心数学模型对初始中心进行优化,先用一种改进的遗传算法获得文档集合的近似最优聚簇数K,然后采用网络中心与重心数学模型来获得优化的初始聚类中心点,有效解决了算法对初始聚类中心的敏感性,取得了较好的实验结果。在实践应用阶段结合不同的医疗业务场景设计制定不同的应用规则模型,并通过智能检查预约时效分析、输血质量智能评价、手术风险预测分析、辅助诊断推荐等实践应用检测该算法的运行效果,取得了良好的运用结果。
In order to solve the problem of the lack of intelligent application in hospital,this paper puts forward a kind of intelligent application related to hospital aided diagnosis and treatment.This algorithm combines an improved genetic algorithm and network center mathematical model to optimize the initial center.Firstly,an improved genetic algorithm is used to obtain the approximate optimal clustering number k of document set,and then the network center and center of gravity mathematical model are used to obtain the optimized initial clustering center,which effectively solves the sensitivity of the algorithm to the initial clustering center,and achieves good experiments result.In the practical application stage,different application rule models are designed and formulated in combination with different medical business scenarios,and the operation effect of the algorithm is tested through practical applications such as intelligent inspection appointment timeliness analysis,intelligent evaluation of blood transfusion quality,surgical risk prediction analysis and auxiliary diagnosis recommendation,and good application results are achieved.
作者
曹磊
刘强
姚辉
CAO Lei;LIU Qiang;YAO Hui(Information Center,The First Affiliated Hospital of Nanchang University,Nanchang 330006,China;Human Resources and Social Security Dept.of Jiangxi Prov.,Nanchang 330025,China)
出处
《计算机与现代化》
2020年第12期38-42,共5页
Computer and Modernization
基金
江西省青年科学基金资助项目(20181bab211015)。
关键词
智慧医院
聚类算法
辅助决策
smart hospital
clustering algorithm
assistant decision-making
