摘要
基于医院编码员合理配置与平衡调整的目的,该文采用改进遗传算法优化神经网络的方法,构建了医院编码员预测模型。通过收集编码员历史及当前数据,分析了编码业务工作量、编码员工作效率等关键影响因素的作用机制。经过神经网络构建、遗传算法改进及优化等步骤,模型成功推导出编码员数量变化规律。测试显示,新模型预测误差从±10人降至±6人,误差减少约4人,精度显著提升,为医院决策提供支持。该研究成功构建并验证基于改进遗传算法优化神经网络的预测模型,克服了传统方法的局限,提升了预测精度,为编码员合理配置提供了科学依据。
For the purpose of rational allocation and balanced adjustment of hospital coders,this paper adopts an improved genetic algorithm to optimize the neural network algorithm and constructs a hospital coder prediction model.By collecting historical and current data of coders,the mechanism of key influencing factors such as coding workload and coder work efficiency has been determined.After steps such as neural network construction,genetic algorithm improvement,and optimization,the model successfully derived the variation pattern of the number of coders.Tests have shown that the prediction error of the new model has decreased from±10 people to±6 people,reducing by about 4 people and significantly improving accuracy,providing support for hospital decision-making.This study successfully constructed and validated a prediction model based on improved genetic algorithm optimized neural network,overcoming the limitations of traditional methods,improving prediction accuracy,and providing scientific basis for the rational configuration of coders.
作者
叶炼
黄容
邓单
曹云帆
冯欢
邱立志
YE Lian;HUANG Rong;DENG Dan;CAO Yunfan;FENG Huan;QIU Lizhi(Sichuan Science City Hospital,Mianyang 621000,China)
出处
《电子设计工程》
2025年第20期191-196,共6页
Electronic Design Engineering
基金
四川省医院协会项目(YG2334)。
关键词
改进遗传算法
神经网络构建
医院编码员
预测模型
improved genetic algorithm
neural network construction
hospital coder
prediction model
