75岁以上女患者甲状腺预警模型数据
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参与分级健康体检项目的75岁以上女性患者群体，进行甲状腺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： TSH指标数值，TSH ≤7mIU/L为【正常】，标记为“/”；TSH＞ 7mIU/L为【异常】，标记为“！”。三.生成模型数据：生成健康状态预警特征模型GLUW(TSH)＝α1×glu_7_min(TSH)+ α2×glu_7_plus(TSH)，其中α1-α2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(TSH)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，TSH是甲状腺检测的指标项目促甲状腺激素，为其代称，GLUW(TSH)指不同数值区间的人员数，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 stratified analysis of thyroid health test results for female patients aged 75 and above who participated in graded health examination programs. The work aims to construct indicator models, collect and organize physical examination data, perform early warning and grading processing on the corresponding indicator data of examinees, and issue early warnings for relevant health issues. The analysis results serve as an important basis for the subsequent medical care and rehabilitation 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 arrange free medical clinics, etc. For people of different age groups, the application of algorithm-generated early warning feature models varies, so scenario-based algorithms are processed by age strata. 1. Data Collection and Import: Statistically organize the data of physical examination patients who participated in [graded health examination] and are [female patients aged 75 and above], and import them 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 the thyroid-stimulating hormone (TSH) indicator value, TSH ≤7 mIU/L is defined as [normal], marked with "/"; TSH >7 mIU/L is defined as [abnormal], marked with "!". 3. Model Generation: Construct the health status early warning feature model GLUW(TSH) = α₁×glu_7_min(TSH) + α₂×glu_7_plus(TSH), where α₁ and α₂ are model weights. The weight values are obtained via the expert estimation method: relevant domain experts comprehensively judge the importance of each indicator based on empirical knowledge, and the weights are derived through statistical processing in each study. Conduct comprehensive analysis of the GLUW(TSH) values to perform early warning of carotid intima-related diseases 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 frequencies, carry out free medical clinics, and for government authorities to understand the health status of local residents, so as to formulate corresponding follow-up and management strategies. Note that GLUW refers to the early warning feature model formula, and TSH is the abbreviation for thyroid-stimulating hormone, a thyroid test indicator. GLUW(TSH) represents the number of individuals in different numerical intervals. After each physical examination, the relevant data model is calculated and imported into another model database for fluctuation analysis. When a fluctuation exceeding 50% is detected, a reusability study of the data content shall be conducted to observe whether any abnormal situation exists.
