可解释的人工智能模型预测了2型糖尿病患者全因死亡风险
Olga Vershinina1,2, Jacopo Sabbatinelli3,4, Anna Rita Bonfigli5
1Research Center in Artificial Intelligence, Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University, Nizhny Novgorod, Russia.
Frontiers in endocrinology
|November 3, 2025
概括
机器学习准确预测2型糖尿病患者的死亡风险. 该工具有助于识别高风险个体,以优化治疗策略.
科学领域:
- 内分泌学 在内分泌学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 2型糖尿病 (T2DM) 是一种广泛的慢性疾病,影响预期寿命.
- 准确的全因死亡风险预测对于个性化的T2DM管理至关重要.
研究的目的:
- 开发和验证机器学习模型,用于预测T2DM患者的全因死亡率.
- 为了提高模型的解释性,使用SHAP进行临床应用.
主要方法:
- 对554名T2DM患者队列的分析,随访时间长达16.8年.
- 训练和验证多个机器学习模型,包括额外生存树 (EST).
- 应用SHAP来解释表现最好的模型的预测.
主要成果:
- 使用十个特征的EST模型显示出强大的预测性能 (C-统计值0.776).
- 在5年,10年,15年和16.8年的死亡率预测 (0.86,0.80,0.841,0.826) 中达到高AUC值.
- SHAP分析提供了对个体死亡风险预测的见解.
结论:
- 开发的ML模型准确预测T2DM患者的死亡风险.
- 可解释的输出方便潜在的床上使用风险分层.
- 支持高风险T2DM个体及时优化治疗.
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