65岁以下男性患者葡萄糖指标预警模型数据

季度内，参与分级健康体检项目的65岁以下男性体检患者群体，进行葡萄糖数据的检测结果划定分析，进行指标模型的构建，体检数据的收集整理分析，通过葡萄糖测定，对体检主体的相应指标数据进行预警分级处理，进行健康状况预警分析,针对糖尿病等疾病进行预警，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。一、统计参与【分级健康体检】的【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：结果数值大于3.9mmol/L小于6.1mmol/L，健康状态为【正常】；结果数值大于6.1mmol/L小于7.0mmol/L，健康状态为【预警】；结果数值大于7.0mmol/L，健康状态为【异常】。三.生成模型数据：生成健康状态预警特征模型GLUW(t)，公式为GLUW(t)=α1×glu_3.9_6.1(t)+ α2×glu_6.1_7.0(t)+α3×glu_7.0_plus(t)，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(t)数值，针对数据包所涉及对象，进行糖尿病等疾病的预警，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。Within a given quarter, for the male patient population aged under 65 who participated in the tiered health examination program, this study conducts classification and analysis of glucose test results, constructs indicator models, collects and organizes physical examination data, performs early warning grading of relevant indicator data for examinees via glucose measurement, carries out health status early warning analysis, and issues early warnings for diseases such as diabetes. The analysis results serve as a critical basis for the subsequent medical care and health management of 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 and organize free medical clinics, etc. 1. Data Collection: Import the medical records of male examinees participating in the [Tiered Health Examination] into the database, including: name, age, physical examination diagnosis, examination type, package name, indicator items, examining physician, abnormal flag, and other relevant information. 2. Status Grading: Classify the health status as [Normal] when the glucose test result is greater than 3.9 mmol/L and less than 6.1 mmol/L; as [Warning] when the result is greater than 6.1 mmol/L and less than 7.0 mmol/L; and as [Abnormal] when the result is greater than 7.0 mmol/L. 3. Model Generation and Application: Develop the health status early warning feature model GLUW(t), with the formula: GLUW(t) = α₁ × glu_3.9_6.1(t) + α₂ × glu_6.1_7.0(t) + α₃ × glu_7.0_plus(t), where α₁-α₃ are the model weights. The weight values are determined via expert estimation: relevant domain experts comprehensively judge the importance of each indicator based on empirical knowledge, and the weights are obtained through statistical processing in each study. By comprehensively analyzing the GLUW(t) values, early warnings for diseases such as diabetes can be issued for the subjects covered in the dataset. This study provides a demonstration for physical examination result analysis in the industry, and offers data support for hospitals to adjust physical examination items and frequencies, organize free medical clinics, as well as for government authorities to understand the health status of residents in the region.