基于机器学习的急性病和老年患者医院死亡率的风险预测
Xinyuan Wang1, Lingyu Xu1, Chen Guan1
1Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Frontiers in medicine
|August 30, 2024
概括
机器学习模型可以预测急性病 (AKD),急性损伤 (AKI) 和老年人的死亡率. 这些模型,特别是光梯度增强机,为早期干预和改善患者护理提供了宝贵的工具.
科学领域:
- 老年学是一门学科.
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能在医学中的应用
背景情况:
- 急性损伤 (AKI) 在老年人中很常见,增加急性病 (AKD) 和死亡率的风险.
- AKD包括AKI后的不良结果,需要用于风险分层的预测工具.
研究的目的:
- 开发和验证用于预测老年患者AKD,AKI和死亡率的机器学习模型.
- 使用可解释的人工智能方法识别AKD的关键预测因素.
主要方法:
- 对22005名老年患者 (≥65岁) 的病历进行了回顾性分析.
- 开发了八个机器学习模型,其中光梯度增强机 (LGBM) 被选为表现最佳的模型.
- 模型性能使用接收器操作特征曲线 (AUC) 下的面积和通过Shapley添加式解释 (SHAP) 的特征重要性进行评估.
主要成果:
- 20.15%的人患有AKD,18.18%的人患有AKI,3.94%的人死亡.
- 在LGBM模型中,AKD的AUC为0.760,AKI为0.767,死亡率为0.927.
- 最重要的AKD预测因素包括AKI阶段,白蛋白,乳酸脱酶,阿司匹林和冠状动脉心脏病.
结论:
- 在老年人中,LGBM模型准确地预测了AKD,AKI和死亡率,使得及时的干预成为可能.
- 开发了一个在线预测工具,为医疗保健专业人员提供实用的实用工具.
- 建议进行进一步的研究,以对这些预测模型进行外部验证和临床整合.
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