75岁以上男患者血常规预警模型数据

参与分级健康体检项目的75岁以上男性患者群体，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【75岁以上男性】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 dataset focuses on the analysis of routine blood test results and construction of indicator models for male patients aged 75 and above who participated in hierarchical health physical examination programs. By collecting, sorting and analyzing physical examination data, and conducting warning grading processing on the corresponding indicator data of the examinees, early warnings are issued for relevant health issues. The analysis results serve as a critical basis for the subsequent medical care and rehabilitation of patients. This framework plays a demonstrative role in physical examination result analysis across the industry, guiding hospitals to adjust the quantity and types of physical examination items and organize free medical clinics, among other measures. As the application of algorithm-generated early warning feature models varies across different age groups, scenario-based algorithms are processed according to age layers.

1. Data Import: Import the physical examination patient data of [male patients aged 75 and above] who participated in [hierarchical health physical examination] into the database, including: name, age, physical examination diagnosis, physical examination type, package name, indicator items, examining physician, abnormality marker, etc.

2. Status Grading: For white blood cell count (WBC): the range 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: 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, α₂, α₃ are the model weights. The weight values are determined via the expert estimation method: relevant experts comprehensively evaluate the importance of each indicator based on empirical knowledge, and the final weights are derived through statistical processing conducted in each study. By comprehensively analyzing the GLUW(WBC) values, early warnings for carotid intima-related diseases are issued for the subjects covered in the dataset. This work provides a demonstration for physical examination result analysis in the industry, and offers data support for hospitals to adjust physical examination items and frequency, carry out free medical clinics, and for government regulatory authorities to understand the health status of local residents, thereby formulating corresponding follow-up and management strategies.

GLUW is the formula of the early warning feature model, WBC is the alias for white blood cell count, and GLUW(WBC) refers to the number of individuals falling into different white blood cell value intervals. After each physical examination, the relevant data model is calculated and stored in another model database for fluctuation analysis. If a fluctuation exceeding 50% is detected, reuse research will be conducted on the data content to observe whether any abnormal situations have occurred.

搜集汇总

数据集介绍

特点

该数据集包含75岁以上男性患者的血常规检测数据，主要用于构建健康预警模型。数据涵盖白细胞数值（WBC）等关键指标，并通过算法规则进行分级和预警分析，适用于体检结果分析和健康管理。