门诊流感样病例第四季度青年人数预测模型数据

针对流感样病例监测，基于桐乡各医疗机构门诊就诊记录，获取全年门急诊就诊人数、流感样病例确诊人数、年龄组及性别分布，根据年龄分为婴幼儿、少年、青年、中年、老年，五个年龄层次，筛选出第四季度青年年龄层次的门诊流感样病例人数进行预测，根据该年龄层次的人数预测，为第四季度青年人群流感防控工作及哨点医院监测工作提供新的思路和方法，并合理配置医疗资源。数据采集：从医疗机构获取匿名化样本统计数据，包括每日门急诊就诊人数、流感样病例症状人数、年龄组及性别分布。对数据进行预处理。特征工程：生成针对年龄组大于15小于25的特征AGW_young(t)=α×age_15_25(t)，α为模型权重。
生成性别特征GW_young(t)，仅针对年龄组大于15小于25：
GW_young(t)=γ1×male_cases_15_25(t)+γ2×female_cases_15_25(t)，γ1,γ2为模型权重。模型构建：构建一个专门预测年龄组大于15小于25的流感样病例人数的模型 F_young(t)=w1×C_young(t−1)+⋯+wn×C_young(t−n)+β1*AGW_young(t)+β2*GW_young(t)，C_young(t)代表当天年龄组大于15小于25的流感样病例人数；C_young(t−n)为t−n日年龄组大于15小于25的流感样病例人数；wn为历史数据权重；β1和β2是衍生特征权重。构建一个预测年龄组大于15小于25的流感样病例人数的模型 sum_young_se1=∑C_young(t)，时间t在第四季度内

For influenza-like illness (ILI) surveillance, based on outpatient visit records from various medical institutions in Tongxiang, this study collected annual data including the number of outpatient and emergency visits, confirmed ILI cases, and age and gender distributions. The study population was divided into five age groups: infants, adolescents, young adults, middle-aged adults, and elderly adults. The number of outpatient ILI cases among the young adult age group in the fourth quarter was selected for prediction. Such prediction can provide novel ideas and approaches for influenza prevention and control among the young population and sentinel hospital surveillance in the fourth quarter, and support the rational allocation of medical resources.

Data Collection: Anonymized statistical sample data was obtained from medical institutions, including daily outpatient and emergency visit volume, number of ILI symptomatic cases, age and gender distributions. The collected data was preprocessed.

Feature Engineering: Generate the feature $AGW_{ ext{young}}(t)$ for the 15-25 age group: $AGW_{ ext{young}}(t) = alpha imes age_{15\_25}(t)$, where $alpha$ is the model weight. Generate the gender feature $GW_{ ext{young}}(t)$ exclusively for the 15-25 age group: $GW_{ ext{young}}(t) = gamma_1 imes male\_cases_{15\_25}(t) + gamma_2 imes female\_cases_{15\_25}(t)$, where $gamma_1$ and $gamma_2$ are model weights.

Model Construction: Construct a model dedicated to predicting the number of ILI cases in the 15-25 age group: $F_{ ext{young}}(t) = w_1 imes C_{ ext{young}}(t-1) + dots + w_n imes C_{ ext{young}}(t-n) + eta_1 imes AGW_{ ext{young}}(t) + eta_2 imes GW_{ ext{young}}(t)$, where $C_{ ext{young}}(t)$ represents the number of ILI cases in the 15-25 age group on day $t$; $C_{ ext{young}}(t-n)$ represents the number of ILI cases in the 15-25 age group on day $t-n$; $w_n$ is the weight of historical data; $eta_1$ and $eta_2$ are the weights of derived features. Construct another prediction model for ILI cases in the 15-25 age group: $ ext{sum\_young\_se1} = sum C_{ ext{young}}(t)$, where time $t$ falls within the fourth quarter.

搜集汇总

数据集介绍

特点

该数据集专注于预测第四季度15-25岁青年人群的流感样病例人数，包含详细的年龄和性别分类数据，每日更新，旨在为流感防控和医疗资源配置提供支持。