紧急部门的COVID-19诊断预测的多模态深度学习:一个双中心研究
Franca Dipaola1, Mauro Gatti2, Alessandro Giaj Levra3,4
1Internal Medicine, Humanitas Clinical and Research Center, IRCCS, Humanitas Research Hospital, Humanitas University, Via A. Manzoni, 56, 20089, Rozzano, Milan, Italy.
Scientific reports
|July 5, 2023
概括
预测COVID-19患者的结果至关重要. 结合患者病史和实验室数据的新型人工神经网络模型准确预测了30天的死亡率和ICU入院,有助于紧急部门的决策.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 计算生物学 计算生物学
背景情况:
- 预测进入急诊室 (ED) 的COVID-19患者的临床恶化是复杂的.
- 电子医疗记录包含有价值的文本和表格数据用于预测建模.
研究的目的:
- 开发和评估一个整合文本和表格数据的人工神经网络模型,用于预测COVID-19患者的30天死亡率和ICU入院情况.
- 将组合模型的性能与仅使用表格数据的模型进行比较.
主要方法:
- 开发一个TensorFlow表格-文本人工神经网络模型.
- 包括来自米兰地区两个医院的1296名COVID-19患者 (2020年2月20日至5月5日).
- 使用患者病史,体检,放射学报告 (文本) 和年龄,肌素,C反应蛋白,血红蛋白,血小板计数 (表格) 作为预测因素.
主要成果:
- 结合模型的30天死亡预测性能略有改善,与仅表格模型相比 (AUC 0.87 ± 0.02,F1 0.62 ± 0.10,MCC 0.52 ± 0.04).
- 与表式模型相比,综合模型在基于马修斯相关系数 (MCC) 的ICU入院预测方面表现优异 (p < 0.024).
结论:
- 结合电子医疗报告中的文本和表格数据的混合模型可以有效预测COVID-19患者的预后.
- 这种方法可能会增强管理COVID-19患者的急诊室医生的临床决策.
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