70-74岁男患者甲状腺预警模型数据

参与分级健康体检项目的70-74岁男性患者群体，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【70-74岁男性】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： 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 study focuses on the 70-74 year-old male patient cohort participating in graded health checkup programs, conducting stratification analysis of thyroid health test results, constructing indicator models, collecting, organizing and analyzing physical examination data, performing early warning and grading processing on the corresponding indicator data of examinees, and issuing 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 clinics, etc. Since the application of algorithm-generated early warning feature models varies across different age groups, scenario algorithms are processed by age strata.
1. Data Import: Statistics and importation of physical examination patient data of 70-74 year-old males participating in the "graded health checkup" program into the database, including: name, age, physical examination diagnosis, physical examination type, package name, indicator items, examining physician, abnormal marker, etc.
2. Status Stratification: For the TSH (Thyroid-Stimulating Hormone) indicator value, TSH ≤7 mIU/L is classified as "normal" and marked with "/"; TSH >7 mIU/L is classified as "abnormal" and marked with "!".
3. Model Data Generation: The health status early warning feature model GLUW(TSH) = α₁×glu_7_min(TSH) + α₂×glu_7_plus(TSH) is constructed, 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. By comprehensively analyzing the GLUW(TSH) values, early warning for carotid intima-related diseases is conducted for the subjects covered in the dataset. This plays a demonstrative role in physical examination result analysis within the industry, provides data support for hospitals to adjust physical examination items and frequency, carry out free clinics, and for government authorities to understand the health status of residents in the region, and helps formulate corresponding follow-up and management strategies.
GLUW is the formula of the early warning feature model, and TSH is the abbreviation of Thyroid-Stimulating Hormone, a thyroid test indicator. GLUW(TSH) refers to the number of subjects in different value ranges. After each physical examination, the relevant data model is calculated and stored in another dedicated model database for fluctuation analysis. When a fluctuation greater than 50% is detected, a reuse study of the data content shall be conducted to observe whether any abnormal situation occurs.

搜集汇总

数据集介绍

特点

该数据集包含70-74岁男性患者的甲状腺健康检测数据，用于构建预警模型并进行健康状态分级，支持医院和政府主管部门的健康管理决策。