在2型糖尿病中使用综合生物标志物进行基于机器学习的糖尿病病预测建模:单中心回顾性研究
Ying Zhu1, Yiyi Zhang1, Miao Yang1
1Department of Endocrinology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China.
概括
机器学习模型可以使用生物标志物预测2型糖尿病患者的糖尿病病 (DN) 风险. 通过及时干预,早期识别DN风险可以改善患者的治疗结果.
科学领域:
- 生物医学信息学 生物医学信息学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 内分泌学 在内分泌学.
背景情况:
- 糖尿病病 (DN) 是糖尿病的严重并发症,需要早期识别风险进行干预.
- 预测DN发育对于管理糖尿病和预防病进展至关重要.
研究的目的:
- 开发和验证用于预测糖尿病病风险的机器学习 (ML) 模型.
- 确定关键的生物标志物和与2型糖尿病患者DN发展相关的临床因素.
主要方法:
- 一组2184名没有DN的参与者被跟踪36个月,并记录了DN的发展.
- 机器学习算法,包括支持矢量机 (SVM),用于预测建模.
- 模型性能在468名参与者的独立数据集上得到验证.
主要成果:
- 血清肌素,尿素的升高和估计的淋巴球过率 (eGFR) 的降低与DN的发展有关.
- 糖尿病视网膜病变和外围神经病变是DN的显著预测因素.
- 在测试组中,SVM模型实现了高性能,AUC为0.83,F1得分为0.82.
结论:
- 将ML与基线临床和生物标志物数据相结合,可以有效预测2型糖尿病的DN风险.
- 这种方法有助于早期识别有风险的个体,以便及时干预.
- 经过验证的ML模型为糖尿病病的积极管理提供了一个有前途的工具.
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