70-74岁男患者血常规预警模型数据
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参与分级健康体检项目的70-74岁男性患者群体，进行血常规健康检测结果划定分析，进行指标模型的构建，体检数据的收集整理分析，对体检主体的相应指标数据进行预警分级处理，针对相关健康问题进行预警，分析结果是患者后续医疗康养的重要依据，该模式对于行业内体检结果分析具有示范作用，引导医院调整体检项目数量，种类，安排义诊等。不同年龄段的人群，根据算法生成的预警特征模型应用不同，故将场景算法分年龄层处理。 一、统计参与【分级健康体检】的【70-74岁男性】体检患者资料导入数据库，包括：姓名、年龄、 体检诊断、体检类型、套餐名称、指标项目、检查医生、异常标记等。二、状态分级： 白细胞数值（WBC），3.5x（10的九次方）/L≤WBC ≤ 9.5x（10的九次方）/L为【正常】，标记为“/”；WBC＜3.5x（10的九次方）/L为【过低】，标记为“-”,WBC＞ 9.5x（10的九次方）/L为【过高】，标记为“＋”。三.生成模型数据：生成健康状态预警特征模型GLUW(WBC)＝α1×glu_3.5_min(WBC)+ α2×glu_3.5_9.5(WBC)+α3×glu_9.5_plus(WBC)，其中α1-α3为模型权重，权重数值采取专家估测法，由相关领域专家依据经验知识，综合判断各指标的重要性，通过每次研究时的统计处理得到权重。综合分析GLUW(WBC)数值，针对数据包所涉及对象，进行颈动脉内膜相关疾病的预警，从而对行业内体检结果分析进行示范，并为医院调整体检项目，体检频率，开展义诊以及政府主管部门了解该地区居民健康状况提供数据支撑,并制定相应的随访和管理策略。 GLUW是预警特征模型公式，WBC为白细胞数值代称，GLUW(WBC)指不同白细胞数值区间的人员数，在每次体检后，将相关数据模型进行计算，归于另外的模型数据库，进行波动情况分析，出现大于50%的波动时，要针对数据内容进行复用研究，观察是否出现异常情况。
This study focuses on the cohort of male patients aged 70 to 74 who participated in graded health checkup programs. We conducted stratified analysis based on routine blood test results, constructed indicator models, collected and organized physical examination data, performed early warning and grading processing on the indicator data of the examinees, and issued early warnings for relevant health issues. The analysis results serve as an important basis for the subsequent medical care and rehabilitation of the patients. This model has a demonstrative role in physical examination result analysis within the industry, guiding hospitals to adjust the quantity and types of physical examination items and arrange free medical consultations, etc. For populations of different age groups, the application of early warning feature models generated by algorithms varies, so scenario algorithms are processed by age strata. 1. Data Import: Import the physical examination data of patients aged 70-74 who participated in [Graded Health Checkup] into the database, including: name, age, physical examination diagnosis, physical examination type, package name, indicator items, examining physician, abnormality flag, etc. 2. Status Grading: For white blood cell count (WBC): 3.5×10⁹/L ≤ WBC ≤ 9.5×10⁹/L is classified as [Normal], marked with "/"; WBC < 3.5×10⁹/L is classified as [Too Low], marked with "-"; WBC > 9.5×10⁹/L is classified as [Too High], marked with "+". 3. Model Data Generation: Construct the health status early warning feature model GLUW(WBC) = α₁×glu_3.5_min(WBC) + α₂×glu_3.5_9.5(WBC) + α₃×glu_9.5_plus(WBC), where α₁-α₃ are the model weights. The weight values are obtained through expert estimation: relevant domain experts comprehensively judge the importance of each indicator based on empirical knowledge, and the weights are derived via statistical processing in each study. Conduct a comprehensive analysis of the GLUW(WBC) values to perform early warning of carotid intima-related diseases for the subjects included in the dataset. This work provides a demonstration for physical examination result analysis within the industry, and offers data support for hospitals to adjust physical examination items and frequency, carry out free medical consultations, and for government authorities to understand the health status of local residents, thereby formulating corresponding follow-up and management strategies. GLUW refers to the formula of the early warning feature model, WBC is the abbreviation for white blood cell count, and GLUW(WBC) represents the number of subjects in different WBC value ranges. After each physical examination, the relevant data model is calculated and stored in a separate model database for fluctuation analysis. When a fluctuation exceeding 50% is detected, reuse-based research should be conducted on the data content to observe whether abnormal conditions have occurred.
