通过基于人工智能的多式联络数据融合,高效的临床决策过程:一项COVID-19病例研究
Daniel I Morís1,2, Joaquim de Moura1,2, Pedro J Marcos3
1Varpa Group, Biomedical Research Institute A Coruña (INIBIC), University of A Coruña, 15006, A Coruña, Spain.
Heliyon
|December 6, 2024
概括
一种新的自动化方法使用人工智能通过结合患者数据和X射线图像来预测COVID-19住院和死亡风险. 这种方法可以改善临床决策和资源管理.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 临床决策支持系统 临床决策支持系统
背景情况:
- 由于COVID-19的流行,需要有效的资源管理和风险分层.
- 计算机辅助诊断帮助临床医生识别高风险患者.
研究的目的:
- 开发一种使用多式联络数据融合的COVID-19风险估计完全自动化的方法.
- 评估COVID-19患者住院和死亡的风险.
主要方法:
- 利用多式联络数据融合,结合胸部X射线图像中的临床特征和深度特征.
- 开发了一种新,高效,全自动的机器学习模型.
- 使用接收器操作特征曲线下的面积 (AUC-ROC) 评估模型性能.
主要成果:
- 在预测住院 (AUC-ROC 0.8452 ± 0.0133) 和死亡率 (AUC-ROC 0.8285 ± 0.0210) 方面取得了高性能.
- 确定了每个风险场景的关键特征,显示了临床和成像数据的不同作用.
- 证明多式联运数据融合优于单源数据方法.
结论:
- 开发的方法有效地帮助临床决策对COVID-19风险分层.
- 多式联网数据融合提供了优势,即使具有减少的功能集,有利于资源有限的设置.
- 该方法显示了在管理其他临床情景方面具有更广泛应用的潜力.
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