使用多层感知神经网络对患有呼吸道瘤的重症监护患者的结果预测
1Complexo Hospitalario Universitario de Pontevedra, Pontevedra, PO, Spain.
Einstein (Sao Paulo, Brazil)
|September 20, 2023
概括
一个人工神经网络准确地预测了重症肺癌患者的住院死亡率. 这种新工具的性能优于现有的评分系统,为瘤重症监护室患者提供更好的歧视和有价值的评估.
科学领域:
- 在瘤学瘤学.
- 关键护理医学 关键护理医学
- 人工智能在医学中的应用
背景情况:
- 在重症监护室 (ICU) 癌症患者的死亡率有很大差异,这表明患者的特征和预后是独特的.
- 现有的疾病严重程度和并发症评分缺乏全面纳入癌症特异因素,限制了它们对死亡风险的预测准确性.
研究的目的:
- 开发一种人工神经网络 (ANN) 模型,用于预测 ICU 住院的成年呼吸道瘤患者的住院死亡率.
- 根据已建立的评分系统,评估开发的ANN的性能.
主要方法:
- 利用了Beth Israel Deaconess医疗中心1,221次重症监护室住院的数据.
- 开发了一个ANN模型,结合了与肺癌死亡率相关的实验室,人口,器官支持和临床参数.
- 使用歧视和校准指标,比较了ANN的预测性能与六个疾病严重性得分和一个并发性得分.
主要成果:
- 该ANN (多层感知子) 实现了0.885的接收器运行特征曲线 (AUROC) 下的面积,显著优于传统系统 (AUROC <0.74).
- 对于ANN,精度回忆曲线下的面积 (AUPRC) 为0.731,而传统系统的面积为≤0.482.
- 与OASIS (0.148),SAPS III (0.163) 和SAPS II (0.154) 相比,ANN表现出一个更高的障碍得分 (0.109).与SAPS III (0.163) 和SAPS II (0.154) 相比,ANN表现出一个更高的障碍得分 (0.109).
结论:
- 开发的人工神经网络在预测重症肺癌患者的住院死亡率方面表现出极好的区别.
- 这种ANN模型显示出作为一种有价值的工具,用于在重症监护室对这些患者的风险评估和管理.
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