基于可解释的机器学习算法,预测急性胰腺炎重症监护病人的住院死亡率
Wensen Ren1,2, Kang Zou1,2, Shu Huang3,4
1Department of Gastroenterology, the Affiliated Hospital of Southwest Medical University.
Journal of clinical gastroenterology
|September 15, 2023
概括
这项研究开发了一种可解释的机器学习模型,用于预测急性胰腺炎 (AP) 患者在重症监护室 (ICU) 的死亡率. 高斯的天真贝叶斯模型准确地识别了高风险患者,以便及时进行干预.
科学领域:
- 关键护理医学 关键护理医学
- 医疗保健中的机器学习
- 预测分析是一种预测分析.
背景情况:
- 急性胰腺炎 (AP) 是一种危及生命的疾病,需要早期风险分层.
- 确定患有AP的高死亡风险的重症监护病房 (ICU) 患者对于及时干预至关重要.
研究的目的:
- 开发一种可解释的机器学习模型,用于预测患有AP的ICU患者的住院死亡率.
- 使用外部数据集验证模型的性能和通用性.
主要方法:
- 利用了来自MIMIC-IV和eICU-CRD数据库的数据,包括人口统计,生命体征和实验室结果.
- 雇佣了变量选择的最少绝对收缩和选择操作员 (LASSO).
- 开发并选了九个机器学习模型,选择了高斯朴素贝叶斯 (GNB) 作为最佳.
- 使用AUC,精度,灵敏度,特异性,决策曲线和校准图表来评估模型有效性.
- 应用了沙普利的附加解释值来解释模型的解释性.
主要成果:
- 在GNB模型中,AUC达到0.840 (MIMIC-IV) 和0.862 (eICU-CRD).
- 在两个数据集中都观察到高精度,灵敏度和特异性.
- 死亡率的关键预测因素包括红细胞分布宽度,血液氧和度,血液尿素和SOFA分数.
结论:
- 在预测AP患者死亡率方面,GNB模型展示了强大的性能和通用性.
- 这种可解释的AI工具可以有效地识别高风险的ICU患者,以改善临床管理.
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