70-74岁男性颈动脉超声波预警模型数据
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参与分级健康体检项目的70-74岁以上男性体检患者群体，进行颈动脉超声波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。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： 颈动脉内膜中层厚度（IMT），IMT ≤ 1.0mm为【正常】；1.5mm≥IMT＞1.0mm为【增厚】,IMT＞ 1.5mm为【预警】。三.生成模型数据：生成健康状态预警特征模型GLUW(IMT)＝α1×glu_1.0_min(IMT)+ α2×glu_1.0_1.5(IMT)+α3×glu_1.5_plus(IMT)，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(IMT)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。
Male patients aged 70–74 years who participated in the tiered health examination program were enrolled in this study. Carotid ultrasound health detection results were analyzed for classification, an indicator-based model was constructed, and physical examination data were collected, organized and analyzed. Early warning and grading processing were performed on the corresponding indicator data of the examinees to issue 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 plays a demonstrative role in physical examination result analysis within the healthcare industry, guiding hospitals to adjust the quantity and types of physical examination items and arrange free medical clinics, among other measures. 1. Data Collection: Statistical data of male examinees participating in the "Tiered Health Examination" were imported into the database, including: name, age, physical examination diagnosis, examination type, package name, indicator items, examining physician, abnormal markers, etc. 2. Status Grading: For carotid intima-media thickness (IMT): - Normal: IMT ≤ 1.0 mm; - Thickened: 1.0 mm < IMT ≤ 1.5 mm; - Warning: IMT > 1.5 mm 3. Model Construction and Application: A health status early warning feature model GLUW(IMT) was established, formulated as: GLUW(IMT) = α₁ × glu_1.0_min(IMT) + α₂ × glu_1.0_1.5(IMT) + α₃ × glu_1.5_plus(IMT) where α₁, α₂ and α₃ are the model weights. The weights were determined using the expert estimation method: relevant domain experts comprehensively judged the importance of each indicator based on empirical knowledge, and the final weights were obtained through statistical processing conducted in each study. By comprehensively analyzing the GLUW(IMT) values, early warnings for carotid intima-related diseases were issued for the subjects covered in the dataset. This work provides a demonstration for physical examination result analysis in the industry, offers data support for hospitals to adjust physical examination items and frequencies, organize free medical clinics, and for government authorities to understand the health status of residents in the region, and facilitates the formulation of corresponding follow-up and management strategies.
