糖尿病进展的风险预测使用大数据采矿与多种物理检查指标
Xiaohong Chen1, Shiqi Zhou2, Lin Yang1
1Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, People's Republic of China.
概括
这项研究确定了糖尿病前期和糖尿病进展的关键风险和保护因素. 后勤回归模型使用临床指标准确预测糖尿病风险.
科学领域:
- 内分泌学 在内分泌学.
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 糖尿病前期和糖尿病对全球健康构成重大挑战.
- 早期识别风险因素对于及时干预至关重要.
- 预测模型可以帮助风险分层和个性化预防策略.
研究的目的:
- 确定影响从正常葡萄糖耐受性过渡到糖尿病前期的独立风险和保护因素.
- 确定影响从糖尿病前期转变为2型糖尿病的独立风险和保护因素.
- 使用临床指标开发和评估糖尿病风险的预测模型.
主要方法:
- 来自北京大学深医院的健康检查数据的回顾性分析.
- 单变量和多变量逻辑回归用于特征选择,以确定独立的风险和保护因素.
- 开发和评估机器学习模型,包括后勤回归,用于糖尿病预测.
主要成果:
- 对于从正常的进展到糖尿病前期:年龄,PRO,TP,ALT (危险因素);GLB,HDL.C (保护因素). 后勤回归实现了0.78 AUC.
- 对于从糖尿病前期到糖尿病的进展:年龄,性别,BMI,SBP,U.GLU,PRO,ALT,TG (危险因素);AST (保护因素). 后勤回归实现了0.74的AUC.
- 后勤回归在两个过渡的评估模型中显示出优异的性能.
结论:
- 临床指标如年龄,PRO,TP,ALT,GLB和HDL.C是糖尿病前期发展的重要预测因素.
- 包括年龄,性别,BMI,SBP,U.GLU,PRO,ALT,TG和AST在内的因素对于预测糖尿病从前糖尿病开始至关重要.
- 后勤回归模型提供了基于常规收集的健康数据的糖尿病风险预测的强大和可解释的方法.
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