70-74岁女患者血常规预警模型数据

参与分级健康体检项目的70-74岁女性患者群体，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岁女性】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。

A cohort of 70–74-year-old female patients who participated in graded health checkup programs was selected for this study. The work includes analysis of blood routine test results, construction of indicator models, collection and organization of physical examination data, early warning grading processing of relevant indicator data of examinees, and early warning for related health problems. The analysis results serve as an important basis for subsequent medical care and rehabilitation of the patients. This model plays a demonstrative role in physical examination result analysis within the industry, guiding hospitals to adjust the quantity and types of physical examination items, arrange free medical clinics, and other related operations. Since the application of algorithm-generated early warning feature models varies across different age groups, the scenario-based algorithm is processed by age strata.

1. Data Statistics: Import the physical examination records of 70–74-year-old female participants in the graded health checkup program into the database, including: name, age, physical examination diagnosis, physical examination type, package name, indicator items, examining physician, abnormality markers, etc.
2. Status Grading: For white blood cell count (WBC): 3.5×10⁹/L ≤ WBC ≤ 9.5×10⁹/L is defined as [Normal], marked with "/"; WBC < 3.5×10⁹/L is defined as [Too Low], marked with "-"; WBC > 9.5×10⁹/L is defined as [Too High], marked with "+.
3. Model Data Generation: Generate 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 via expert estimation: relevant domain experts comprehensively judge the importance of each indicator based on empirical knowledge, and the final weights are derived through statistical processing in each study. Comprehensive analysis of GLUW(WBC) values is conducted to perform early warning for carotid intima-related diseases among the subjects involved in the dataset. This work provides a demonstration for physical examination result analysis in the industry, and also offers data support for hospitals to adjust physical examination items and frequency, carry out free medical clinics, and for government authorities to understand the health status of residents in this area, so as to formulate corresponding follow-up and management strategies.

GLUW refers to the early warning feature model formula, WBC is the abbreviation for white blood cell count, and GLUW(WBC) refers to the number of people in different WBC value ranges. After each physical examination, the relevant data model is calculated and categorized into another model database for fluctuation analysis. When a fluctuation greater than 50% occurs, reuse analysis should be conducted on the data content to observe whether abnormal situations have emerged.