通过对多变量因素的人工智能分析来预测COVID-19的严重程度
Yu Fu1, Lijiao Zeng1, Pilai Huang1
1Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China.
Heliyon
|August 14, 2023
概括
这项研究开发了一个使用CT扫描和生物标志物的AI模型来预测严重的COVID-19. 该模型准确识别高风险患者,有助于早期治疗决策,以获得更好的结果.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 流行病学 流行病学
背景情况:
- COVID-19进展至严重疾病是死亡的一个重要原因.
- 对严重的COVID-19的预测模型对于及时的患者管理至关重要.
- 早期识别有风险的患者可以指导治疗策略,提高生存率.
研究的目的:
- 开发和验证一个多变量生存模型来预测严重的COVID-19发病.
- 将早期CT成像与生理指标和生物标志物相结合,以提高预测.
- 评估住院成年患者中严重COVID-19进展的风险.
主要方法:
- 对338名成年COVID-19患者的回顾性招生.
- 基于人工智能的多变量生存模型的开发.
- 早期CT图像,生理指标和生物标志物的分析.
主要成果:
- 确定了关键的危险因素:年龄,BMI,发烧,高血压和糖尿病.
- 严重的COVID-19病例显示器官功能,炎症,氧化和凝血生物标志物的早期异常.
- 综合模型实现了高预测性能 (平均时间依赖的AUC=0.880).
结论:
- 结合CT图像和生物标志物的多变异性生存模型显示,预测严重的COVID-19有很大的潜力.
- 早期预测可以为患有严重疾病风险的患者提供及时的临床干预.
- 医疗数据的AI驱动分析可以提高传染病的预后准确性.
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