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使用人工智能来预测AKI患者的死亡率:系统性审查/元分析

Rupesh Raina^1,2, Raghav Shah^1,3, Paul Nemer⁴

¹Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA.

Clinical kidney journal

|June 21, 2024

概括

机器学习模型在预测急性损伤 (AKI) 患者的死亡率方面表现有希望. 广泛学习系统 (BLS) 和弹性净最终 (ENF) 模型表现出高效率,与其他算法相比.

科学领域:

腎臟病學 (nephrology) 是一種醫學專業.
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

急性损伤 (AKI) 显著增加了患者的发病率和死亡率.
人工智能 (AI) 和机器学习 (ML) 为预测AKI死亡率提供了动态方法.
评估各种ML模型的性能对于改善AKI患者的治疗结果至关重要.

研究的目的:

审查和比较不同机器学习模型在预测急性损伤患者的住院死亡率方面的表现.
确定最有效的AI驱动模型来预测AKI死亡率.

主要方法:

在PubMed,Embase和Web of Science进行了全面的文献搜索.
包括专注于原始研究 (横截面,前性,回顾性) 的研究,评估AI模型的有效性,使用AUC,准确性,灵敏度和特异性等指标.
审查和自我报告的结果被排除在外,没有时间或地理限制.

主要成果:

分析了8项涉及37,032名AKI患者的研究.
广义学习系统 (BLS) 和弹性净结论 (ENF) 模型显示,死亡率预测的曲线下面面积 (AUC) 是最高的[0.852].
拟议临床模型 (PCM) 的AUC最低[0.765]但预测值负值最高,表明其作为排除工具的实用性.

结论:

BLS和ENF模型对于预测AKI患者的住院死亡率是有效的,其性能与其他ML模型相比.
在不同ML模型的性能中存在变化.
需要进一步的研究来验证和完善这些预测模型.

关键词:

急性脏损伤急性脏损伤人工智能的人工智能是人工智能.机器学习是机器学习.死亡率死亡率预测预测预测预测

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