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机器学习和统计模型来预测2型糖尿病全因死亡率:来自英国生物银行研究的结果
Tingjing Zhang1, Mingyu Huang2, Liangkai Chen3
1School of Public Health, Wannan Medical College, Wuhu, China; Institutes of Brain Science, Wannan Medical College, Wuhu, China.
与传统的统计方法相比,机器学习模型在2型糖尿病患者中更好地预测了全因死亡率. 一个新的工具有助于个性化的糖尿病护理管理.
科学领域:
- 生物医学信息学 生物医学信息学
- 心血管研究研究心血管研究
- 内分泌学 在内分泌学.
背景情况:
- 2型糖尿病 (T2DM) 是所有原因死亡的主要危险因素.
- 准确预测死亡风险对于个性化患者管理至关重要.
- 传统的统计模型可能无法完全捕捉T2DM死亡率预测的复杂性.
研究的目的:
- 将当代机器学习 (ML) 模型的性能与用于预测T2DM患者全因死亡率的统计模型进行比较.
- 为临床应用开发一个用户友好的死亡风险预测工具.
主要方法:
- 来自英国生物库的22579名T2DM患者的前性队列研究.
- 考克斯比例危险,随机生存森林 (RSF),梯度增强 (GB) 生存,DeepSurv和DeepHit模型的评估.
- 随访时间中位数为9年,记录了2665例死亡事件.
主要成果:
- 包括DeepHit在内的ML模型在预测全因死亡率方面表现优于Cox模型 (C指数为0.72-0.73对比0.71).
- 与考克斯模型相比,深度学习模型表现出优异的校准和较低的Brier分数.
- 一个基于DeepHit的在线预测工具被开发用于患者护理 (http://123.57.42.89:6006/).
结论:
- 机器学习模型为T2DM的全因死亡率提供了比统计模型更好的预测性能.
- 这些发现强调了ML在改善个性化医疗保健和糖尿病管理中的风险分层方面的潜力.
- 开发的预测工具可以促进临床决策和患者护理.
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