70-74女患者颈动脉超声波预警模型数据

参与分级健康体检项目的70-74岁女性体检患者群体，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。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： 颈动脉内膜中层厚度（IMT），IMT ≤ 1.0mm为【正常】，标记为“-”；1.5mm≥IMT＞1.0mm为【增厚】，标记为“√”,IMT＞ 1.5mm为【预警】，标记为“！”。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。

Targeting a cohort of female patients aged 70–74 who underwent tiered health checkups, this study conducts delineated analysis of carotid ultrasound health examination results, constructs indicator-based models, collects and analyzes physical examination data, performs early warning and grading processing on relevant indicator data of examinees, and issues warnings for related health issues. The analysis results serve as a critical basis for 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 and organize free medical clinics, etc.

Since the application of algorithm-generated early warning feature models varies across different age groups, the scenario-based algorithms are processed by age strata.

1. Data Import and Statistics: Import the medical records of female patients aged 70–74 who participated in tiered health checkups into the database, including: name, age, physical examination diagnosis, examination type, package name, indicator items, examining physician, abnormal marker, etc.

2. Condition Grading: For carotid intima-media thickness (IMT):
- IMT ≤ 1.0 mm is classified as [Normal], marked with "-";
- 1.0 mm < IMT ≤ 1.5 mm is classified as [Thickened], marked with "√";
- IMT > 1.5 mm is classified as [Warning], marked with "!";

3. Model Data Generation: Develop a health status early warning feature model GLUW(IMT) = α₁×glu_1.0_min(IMT) + α₂×glu_1.0_1.5(IMT) + α₃×glu_1.5_plus(IMT), where α₁-α₃ are the 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(IMT) values, early warnings for carotid intima-related diseases are 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 frequency, organize free medical clinics, and for government authorities to understand the health status of residents in the region, and helps formulate corresponding follow-up and management strategies.

搜集汇总

数据集介绍

特点

该数据集包含3303条70-74岁女性患者的颈动脉超声波检测数据，用于构建健康预警模型，支持医院和政府进行健康管理和决策。