用人工智能驱动的模型预测急性损伤在儿科心脏重症监护病房
Tiziana Fragasso1, Valeria Raggi2, Davide Passaro3
1Pediatric Cardiac Intensive Care Unit, Bambino Gesù Children's Hospital, IRCCS, Piazza S.Onofrio 4, 00165, Rome, Italy. tiziana.fragasso@opbg.net.
Journal of anesthesia, analgesia and critical care
|October 18, 2023
概括
机器学习可以准确地预测心脏手术后儿童的急性损伤 (AKI). 这种人工智能模型可以早期识别有风险的患者,从而及时实施预防策略,改善脏结果.
科学领域:
- 儿科重症监护医药 儿科重症监护医药
- 生物医学信息学是生物医学信息学.
- 腎臟病學 (nephrology) 是一種醫學.
背景情况:
- 急性损伤 (AKI) 是儿科心脏手术患者的常见并发症,增加了发病率和死亡率.
- 人工智能 (AI) 和机器学习 (ML) 提供了预测AKI等不利结果的潜力.
- 早期预测AKI可能允许及时干预以减轻功能障碍.
研究的目的:
- 开发和验证使用电子健康记录 (EHR) 数据的机器学习 (ML) 模型.
- 在儿科患者心脏手术后至少48小时内预测AKI的持续发生.
- 评估ML模型在预测AKI中的性能.
主要方法:
- 这是一项对2018年8月至2020年2月期间入住心脏重症监护室 (PCICU) 的419名儿科患者的回顾性研究.
- 利用随机森林 (RF) 算法对36个从EHR数据中选择的临床预测因素进行训练.
- 评估模型性能使用曲线下面积接收器操作特征 (AUC ROC) 进行二进制分类和精度/K进行多类分类.
主要成果:
- ML模型表现出高性能:二进制AKI的AUC ROC为0.93,严重AKI的AUCROC为0.99.
- 多类分类实现了高精度:模式AKI为0.95 (K=0.80) 和最大AKI为0.92 (K=0.71).
- 确定的主要预测因素包括肌素,基底肌素,血小板计数,上腺素支持,乳酸脱酶和心肺绕行术的持续时间.
结论:
- 一个经过验证的ML模型可以有效地检测心脏手术后儿童的AKI超过48小时.
- 该模型有助于临床医生识别患有AKI高风险的儿科患者.
- 早期发现有助于实施预防策略,以改善功能障碍的结果.
相关概念视频
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