65岁以下男性患者肺结节预警模型数据

季度内，参与分级健康体检项目的65岁以下男性体检患者群体，进行肺结节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，针对肺结节进行预警，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。一、统计参与【分级健康体检】的【男性】体检患者资料导入数据库，包括：姓名、性别、年龄、体检类型、 体检诊断、项目名称、检查医生、异常标记，诊断结果等。二、状态分级：由医生进行专业判定，诊断结果根据专业经验划分为【正常】、【预警】、【异常】状态，并进行数据的筛选记录。针对【预警】患者进行复查随访，针对【异常】患者安排后续治疗建议；三.生成模型数据：生成健康状态预警特征模型PULW(t)，模型公式为PULW(t)=α1×glu_正常_(t)+ α2×glu_预警_(t)+α3×glu_异常_(t)，其中α1-α3为模型权重，权重具体值由数据负责医生具体判断。综合分析PULW(t)数值并长期记录，当数据整体PULW（t）出现反常波动时加大医疗关注度，针对数据包所涉及对象，进行肺结节的预警，并通过预警模型进一步协调推进诊疗养护计划，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。

Within a given quarter, for the cohort of male examinees under 65 years old who participated in the graded health checkup program, this study conducted analysis on the stratification of pulmonary nodule health test results, constructed indicator models, collected and processed physical examination data, performed early warning grading on the corresponding indicator data of examinees, and implemented targeted early warning for pulmonary nodules. The analysis results serve as a critical basis for the subsequent medical and health care of patients. This framework demonstrates a replicable model for physical examination result analysis in the industry, guiding hospitals to adjust the quantity and types of physical examination items and organize free clinic activities, among other measures.

1. Data Import: Data of [male] examinees participating in [graded health checkup] are collected and imported into the database, including: name, gender, age, physical examination type, physical examination diagnosis, item name, examining physician, abnormal marker, diagnosis result, and other relevant information.
2. Status Grading: Professional determination is made by attending physicians. Diagnosis results are categorized into [Normal], [Warning], and [Abnormal] statuses based on professional clinical experience, with corresponding data screened and recorded. Follow-up reexamination and monitoring are arranged for [Warning] patients, while subsequent treatment recommendations are provided for [Abnormal] patients.
3. Model Data Generation: The health status early warning feature model PULW(t) is constructed, with the formula: PULW(t) = α₁ × glu_normal(t) + α₂ × glu_warning(t) + α₃ × glu_abnormal(t), where α₁-α₃ are the model weights, whose specific values are determined by the data-responsible physician. Comprehensive analysis and long-term recording of PULW(t) values are conducted. Increased medical attention shall be paid when abnormal fluctuations occur in the overall PULW(t) data. Early warning for pulmonary nodules is implemented for the subjects covered in the dataset, and the diagnosis, treatment, and care plan are further coordinated and promoted via the early warning model. This framework not only provides a demonstration for physical examination result analysis in the industry, but also offers data support for hospitals to adjust physical examination items and frequency, organize free clinic activities, and for government competent departments to assess the health status of residents in the local region.

搜集汇总

数据集介绍

特点

该数据集包含1577条65岁以下男性患者的肺结节相关体检数据，用于构建预警模型并进行健康状态分级，为医疗康养和医院调整体检项目提供依据。