可解释的机器学习模型用于心脏手术后急性损伤的早期预测
Jicheng Jiang1, Xinyun Liu1, Zhaoyun Cheng2
1Department of Big Data Center for Cardiovascular Disease, Heart Center of Henan Provincial People's Hospital, Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China.
BMC nephrology
|November 7, 2023
概括
机器学习模型在预测心脏手术后的术后急性损伤 (PO-AKI) 中表现有希望. 虽然后勤回归表现良好,但梯度增强分类器在严重PO-AKI预测方面表现出卓越的准确性.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
背景情况:
- 手术后急性损伤 (PO-AKI) 是心脏手术后的常见并发症,影响患者的结果.
- 准确预测PO-AKI对于及时干预和改善患者管理至关重要.
研究的目的:
- 评估机器学习 (ML) 算法的有效性,以提高PO-AKI的预测.
- 将ML模型的性能与PO-AKI风险分层的常规后勤回归 (LR) 进行比较.
主要方法:
- 一项对2310名成人心脏手术患者进行的回顾性队列研究.
- 使用LR和五个ML算法 (决策树,随机森林,GBC,高斯天真贝叶斯,多层感知子) 开发和验证预测模型.
- 为了模型的可解释性,SHapley添加式扩展 (SHAP) 分析.
主要成果:
- 在44.2%和12.4%的患者中,分别发生了PO-AKI和严重的AKI.
- 该LR模型在PO-AKI预测方面表现强 (AUC为0.812).
- 与其他ML模型和LR相比,渐变增强分类器 (GBC) 算法在严重AKI预测方面表现优越,其中关键预测因素包括手术前血清肌素和超脂血.
结论:
- LR和GBC模型都为PO-AKI预测提供了中等到良好的歧视.
- 在预测严重的PO-AKI时,GBC算法表现出卓越的性能.
- 通过SHAP分析的模型解释性确定了关键预测因素,可能指导临床干预.
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