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Kullaya Takkavatakarn^1,2, Wonsuk Oh³, Ella Cheng⁴

¹Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

|December 20, 2023

概括

在4期慢性病 (CKD4) 中预测功能衰竭至关重要. 机器学习模型,包括人工神经网络,准确预测到末期病 (ESKD) 的进展,帮助患者的护理.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
数据科学数据科学数据科学
生物医学信息学生物医学信息学

背景情况:

末期病 (ESKD) 显著增加了发病率和死亡率.
准确预测从4期慢性病 (CKD4) 到ESKD的进展是具有挑战性的,但对于患者管理至关重要.
在CKD4患者中早期识别风险有助于先进的护理规划,并优化医疗资源分配.

研究的目的:

开发和验证预测模型,以识别高风险的CKD4患者,在三年内进展到ESKD.
为了比较机器学习算法的性能,包括LASSO回归,随机森林,XGBoost和人工神经网络 (ANN) 的ESKD预测.
利用特征重要性分析来了解预测功能衰竭的驱动因素.

主要方法:

利用了3,160名CKD4患者 (2006-2016) 的电子健康记录数据.
开发并验证了四种预测模型:LASSO回归,随机森林,XGBoost和ANN.
使用接收器操作特征曲线下的面积 (AUROC) 评估模型性能,并使用 SHAP 值来解释特征.

主要成果:

在3,160名CKD4患者中,538名 (21%) 进展为ESKD.
所有模型都显示了可比的预测性能,ANN和LASSO回归实现了最高的AUROC0.77.
ANN (0.77,95% CI 0.75-0.79) 和LASSO回归 (0.77,95% CI 0.75-0.79) 的表现略高于随机森林 (0.76) 和XGBoost (0.76).

结论:

开发并验证了多种机器学习模型,用于预测CKD4患者近期功能衰竭.
ANN,随机森林和XGBoost模型显示了类似的,有效的预测能力.
这些模型可以根据个体风险进行定制干预,改善人口健康管理和资源配置.

关键词:

人工智能的人工智能是人工智能.慢性脏疾病慢性脏疾病末期脏疾病末期脏疾病机器学习机器学习预测模型的预测模型.

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