门诊流感样病例第二季度少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，筛选出第二季度少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，为第二季度少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。对流感样病例数据进行预处理。特征工程：生成针对年龄组大于5小于15的特征AGW_young(t)=α×age_5_15(t)，α为模型权重。 生成性别特征GW_young(t)，仅针对年龄组大于5小于15： GW_young(t)=γ1×male_cases_5_15(t)+γ2× female_cases_5_15(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5小于15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) 其中 C_young(t)代表当天年龄组大于5小于15的流感样病例人数；C_young(t−n)为t−n日年龄组大于5小于15的流感样病例人数；wn为历史数据的权重；β1和β2是衍生特征的权重。 构建一个预测年龄组大于5小于15的流感样病例人数的模型 sum_young_se1=∑C_young(t)，时间t在第二季度内。For influenza-like illness (ILI) surveillance, based on outpatient visit records from various medical institutions in Tongxiang, we collected annual outpatient and emergency visit volumes, confirmed ILI case counts, age and gender distributions. The study population was divided into five age groups: infants and toddlers, adolescents, young adults, middle-aged adults, and elderly adults. We selected the number of outpatient ILI cases in the adolescent age group during the second quarter for prediction, aiming to provide novel insights and methodologies for influenza prevention and control among adolescents and sentinel hospital surveillance in the second quarter, as well as to rationally allocate medical resources. Data Collection: Anonymized sample statistical data was obtained from medical institutions, including daily outpatient and emergency visit volumes, number of patients with ILI symptoms, and age and gender distributions. Preprocessing was performed on the ILI-related dataset. Feature Engineering: Generate the feature $AGW_{ ext{young}}(t)$ for the age group of 5 < age < 15: $AGW_{ ext{young}}(t) = alpha imes age_5_15(t)$, where $alpha$ is the model weight. Generate the gender feature $GW_{ ext{young}}(t)$ exclusively for the 5 < age < 15 age group: $GW_{ ext{young}}(t) = gamma_1 imes male_cases_5_15(t) + gamma_2 imes female_cases_5_15(t)$, where $gamma_1$ and $gamma_2$ are model weights. Model Construction: Build a model specifically for predicting the number of ILI cases in the 5 < age < 15 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 5 < age <15 age group on day $t$; $C_{ ext{young}}(t-n)$ represents the number of such cases on day $t-n$; $w_n$ is the weight of historical data; $eta_1$ and $eta_2$ are the weights of the derived features. Build another prediction model for the 5 < age <15 age group: $ ext{sum_young_se1} = sum C_{ ext{young}}(t)$, where time $t$ falls within the second quarter.

搜集汇总

数据集介绍

特点

该数据集提供了桐乡市医疗机构门诊流感样病例的详细数据，重点关注第二季度少年人群的流感样病例预测。数据包括每日就诊人数、年龄组及性别分布，并通过特定算法模型进行预测，旨在为流感防控和医疗资源配置提供支持。