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开发和验证可解释的机器学习模型,以预测哥伦比亚慢性病患者的膜过率

Luis H Rojas¹, Angela J Pereira-Morales¹, William Amador¹

¹Science for Life - S4L SAS, Bogotá, Colombia.

Annals of clinical biochemistry

|September 6, 2024

概括

机器学习模型准确地预测慢性病 (CKD) 患者估计的膜过率 (eGFR) 降低. 这些可解释的模型提供了透明的预测,以帮助临床决策.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
人工智能的人工智能
数据科学数据科学数据科学

背景情况:

机器学习 (ML) 模型提高了医疗保健中的风险预测和临床决策.
在南美洲,迫切需要准确的系统来识别慢性病 (CKD) 的快速进展者.
目前的预测系统缺乏早期识别CKD进展所需的准确性.

研究的目的:

开发可解释的机器学习模型,用于预测6个月,9个月和12个月的估计膜过率 (eGFR).
为CKD患者的结果提供准确和可解释的预测.
解决快速CKD进展者的早期识别方面的差距.

主要方法:

使用极端梯度提升 (XGBoost) 构建了三种对eGFR的预测模型.
包括超过29,000名CKD患者 (阶段1-3b),随访时间为3年.
采用了SHapley添加式解释 (SHAP) 来实现模型的解释性 (全球和本地).
内部和外部验证的模型.

主要成果:

所有开发的模型在开发和外部验证期间都表现出强的性能.
6个月的XGBoost模型表现出最好的表现:内部MAE (6.07),RMSE (78.87);外部MAE (6.45),RMSE (18.94).
降低eGFR的关键预测因素包括基线eGFR,插入eGFR和肌素值.

结论:

成功开发和验证了ML模型,以预测未来的eGFR在各种间隔.
使用SHAP值提供透明的预测,以提高模型的可解释性.
建立了改善CKD进展的早期检测和管理的基础.

关键词:

机器学习是机器学习.慢性脏疾病慢性脏疾病极端的梯度增强了极端的梯度.风险预测风险预测

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