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多功能集成的机器学习预测模型,用于早期病的老年人生活在2型糖尿病中
Tingting Fang1, Yuanyuan Yang2, Feng Zhuo1
1School of Nursing and Rehabilitation, Shandong University, Jinan, China.
Frontiers in endocrinology
|February 6, 2026
概括
一种新的机器学习模型有效地预测了2型糖尿病 (T2DM) 的老年患者的早期糖尿病病 (DN). 它整合了临床数据,传统中医 (TCM) 症状和超声波成像,以改善早期检测和管理.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能的人工智能
- 老年病的医生 老年病的医生
背景情况:
- 糖尿病病 (DN) 是2型糖尿病 (T2DM) 的一个主要并发症.
- 在老年患者中早期发现DN对于有效管理至关重要.
- 整合不同的数据源可以提高预测准确性.
研究的目的:
- 开发和验证用于早期DN预测的多功能机器学习 (ML) 模型.
- 将临床指标,传统中医 (TCM) 症状和超声波成像功能纳入其中.
- 评估T2DM老年患者的模型性能.
主要方法:
- 利用了786名患者的数据,包括临床指标,TCM症状和超声波特征.
- 将数据划分为培训和验证集,用于模型开发.
- 采用机器学习算法,通过准确性,精度,回忆,F1分数和AUC来评估性能.
主要成果:
- 结合临床数据,TCM症状和超声波成像的多功能模型显示出最佳性能.
- 随机森林 (RF) 算法取得了优异的结果,AUC为0.894.
- 亚组分析证实了不同年龄组的强有力的预测性表现 (AUC > 0.7).
结论:
- 这项研究开创了TCM症状和超声波成像的整合,用于在老年T2DM患者中早期DN预测.
- 开发的ML模型在各种年龄人口统计学中展示了强大的预测能力.
- 这些发现支持早期查,预防和干预策略,以改善DN的老年T2DM患者的治疗结果.
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