门诊流感样病例第一季度婴幼儿人数预测模型数据
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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，筛选出第一季度婴幼儿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，为第一季度婴幼儿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0小于5的特征AGW_young(t) =α×age_0_5(t)，其中α为模型权重。 生成性别特征GW_young(t)，仅针对年龄组大于0小于5： GW_young(t)=γ1× male_cases_0_5(t)+γ2× female_cases_0_5(t)，其中γ1,γ2为模型权重。模型构建：构建一个专门预测年龄组大于0小于5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0小于5的流感样病例人数； C_young(t−n)为t−n日年龄组大于0小于5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0小于5的流感样病例人数的模型 : sum_young_se1=∑C_young(t)，时间t在第一季度内
For Influenza-Like Illness (ILI) surveillance, based on outpatient visit records from various medical institutions in Tongxiang, this study collected the annual number of outpatient and emergency visits, number of confirmed ILI cases, age group and gender distribution. The population was divided into five age groups: infants and young children, adolescents, young adults, middle-aged adults, and the elderly. We screened the number of outpatient ILI cases in the infants and young children age group during the first quarter for prediction. Based on the prediction results for this age group, this study aims to provide new ideas and methods for influenza prevention and control work among the infant and young child population, sentinel hospital surveillance during the first quarter, and to rationally allocate medical resources. Data Collection: Obtain anonymized statistical sample data from medical institutions, including daily outpatient and emergency visit counts, number of patients with ILI symptoms, age group and gender distribution. Preprocess the ILI-related data. Feature Engineering: Generate the feature AGW_young(t) = α × age_0_5(t) for the age group of 0 < age < 5, where α is the model weight. Generate the gender feature GW_young(t) exclusively for the 0 < age <5 age group: GW_young(t) = γ₁ × male_cases_0_5(t) + γ₂ × female_cases_0_5(t), where γ₁ and γ₂ are model weights. Model Construction: Construct a model specifically for predicting the number of ILI cases in the 0 < age <5 age group: F_young(t) = w₁ × C_young(t−1) + … + wₙ × C_young(t−n) + β₁ × AGW_young(t) + β₂ × GW_young(t). Here, C_young(t) represents the number of ILI cases in the 0 < age <5 age group on the current day; C_young(t−n) refers to the number of ILI cases in the same age group on day t−n; wₙ is the weight of historical data; β₁ and β₂ are the weights of the derived features. Additionally, construct the calculation for the 0 < age <5 age group within the first quarter: sum_young_se1 = ∑C_young(t), where t falls within the first quarter.
