70-74岁男患者农检生化预警模型数据

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【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： 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。

This study focuses on the cohort of male patients aged 70-74 who participated in hierarchical health examination programs. We conducted classification and analysis of the results of agricultural biochemical health examinations, constructed indicator models, collected, sorted and analyzed physical examination data, performed early warning and grading processing on the corresponding indicator data of examinees, and issued early warnings for relevant health issues. The analysis results serve as an important basis for the subsequent medical treatment 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 medical clinics, etc. Since the application of algorithm-generated early warning feature models varies across different age groups, scenario algorithms are processed by age layers.

1. Statistical import of physical examination patient data of [70-74-year-old males] participating in [hierarchical health examination] into the database, including: name, age, physical examination diagnosis, physical examination type, package name, indicator items, examining physician, abnormality markers, etc.

2. Status grading: For total bilirubin (STB), the range 1.71≤STB ≤17.1μmol/L is defined as [Normal], marked as "/"; STB <1.71μmol/L is defined as [Too Low], marked as "-"; STB >17.1μmol/L is defined as [Too High], marked as "+.

3. Generation of model data: 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) is developed, where α1-α3 are model weights. The weight values are obtained via the expert estimation method: relevant experts in the field comprehensively judge the importance of each indicator based on empirical knowledge, and the final weights are derived through statistical processing in each study. Comprehensive analysis of GLUW(STB) values is conducted to perform early warning for carotid intima-related diseases among the subjects included 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, as well as for government competent departments to understand the health status of local residents and formulate corresponding follow-up and management strategies.

GLUW refers to the early warning feature model formula, while STB is the alias for total bilirubin, an indicator item in agricultural biochemical health examinations. GLUW(STB) denotes the number of individuals falling into different numerical intervals. After each physical examination, the relevant data model is calculated and stored in another model database for fluctuation analysis. When a fluctuation exceeding 50% is detected, reuse research should be conducted on the data content to observe whether any abnormal conditions have occurred.

搜集汇总

数据集介绍

特点

该数据集包含70-74岁男性患者的农检生化健康检测数据，用于构建健康预警模型。数据涵盖体检诊断、指标项目等信息，通过总胆红素（STB）等指标进行健康状态分级预警，支持医院和政府进行健康管理决策。