75岁以上女患者农检生化预警模型数据

参与分级健康体检项目的75岁以上女性患者群体，进行农检生化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： 总胆红素（STB），1.71≤STB ≤ 17.1μmol/L为【正常】，标记为“/”；STB＜1.71μmol/L为【过低】，标记为“-”,STB＞ 17.1μmol/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(STB)＝α1×glu_1.71_min(STB)+ α2×glu_1.71_17.1(STB)+α3×glu_17.1_plus(STB)，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(STB)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，STB是农检生化检测的指标项目总胆红素，为其代称，GLUW(STB)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。

A cohort of female patients aged over 75 who participated in the graded health checkup program conducted classification and delimitation analysis on the results of agricultural inspection biochemical health tests, constructed indicator models, collected, organized and analyzed physical examination data, performed early warning and grading processing on the corresponding indicator data of the examinees, and issued early warnings for relevant health issues. The analysis results serve as an important basis for the subsequent medical care and health management of the 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, arrange free clinics, etc. For people of different age groups, the application of early warning feature models generated by algorithms varies, so the scenario algorithms are processed by age layers.

1. Data Import and Statistical Organization: Import the data of female examinees aged over 75 who participated in the [graded health checkup] program 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 total bilirubin (STB), 1.71 ≤ STB ≤ 17.1 μmol/L is classified as [Normal], marked with "/"; STB < 1.71 μmol/L is classified as [Too Low], marked with "-"; STB > 17.1 μmol/L is classified as [Too High], marked with "+"

3. Generate Model Data: Develop the health status early warning feature model GLUW(STB) = α₁×glu_1.71_min(STB) + α₂×glu_1.71_17.1(STB) + α₃×glu_17.1_plus(STB), where α₁-α₃ are model weights. The weight values are determined through expert estimation: relevant domain experts comprehensively evaluate the importance of each indicator based on their empirical knowledge, and the final weights are obtained via statistical processing conducted in each study. By comprehensively analyzing the GLUW(STB) values, early warnings for carotid intima-related diseases are issued for the subjects covered in the data package. This further demonstrates the value of physical examination result analysis in 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 for the early warning feature model, while STB is the abbreviation for total bilirubin, an indicator item in agricultural inspection biochemical tests. GLUW(STB) refers to the number of people in different value ranges. After each physical examination, relevant data models are calculated and stored in a separate model database for fluctuation analysis. When a fluctuation exceeding 50% is detected, reuse analysis of the data content shall be carried out to check for any abnormal conditions.

搜集汇总

数据集介绍

特点

该数据集包含75岁以上女性患者的农检生化健康检测数据，用于构建健康预警模型，支持医院和政府主管部门的决策。