人工智能在预测严重COVID-19预后方面的表现:系统性审查和元分析
Chu Qin1, Huan Ma1, Mahong Hu2
1School of Public Health, Zhejiang Chinese Medical University, Hangzhou, China.
Frontiers in public health
|August 15, 2024
概括
人工智能 (AI) 模型在预测严重的COVID-19肺炎预后方面表现有希望. 结合临床和放射学数据产生了最好的结果,尽管广泛的临床应用需要进一步的研究.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 公共卫生 公共卫生
背景情况:
- 由于COVID-19引发的肺炎对全球健康构成了重大挑战.
- 在严重的COVID-19病例中预测预后对于患者的结果至关重要.
- 人工智能 (AI) 在预测严重的COVID-19预后方面的作用需要进一步阐明.
研究的目的:
- 系统地审查和元分析AI模型在预测严重COVID-19患者死亡率方面的表现.
- 评估包含各种数据类型的AI模型的预测准确性.
- 确定影响基于人工智能的预后模型性能的因素.
主要方法:
- 在PubMed,Web of Science和Embase数据库中进行全面的文献搜索,截至2023年10月31日.
- 使用PROBAST 2019和Stata 16进行的元分析和偏见评估.
- 包括19项研究与26个AI模型用于预测严重的COVID-19死亡率.
主要成果:
- 整合临床和放射学数据的AI模型显示出卓越的性能,总体灵敏度为0.81和特异性为0.77 (AUC为0.88).
- 来自发达国家的研究和涉及非重症监护病房患者的研究显示,预测特异性明显更高.
- 亚组分析表明,基于地理位置和患者环境,模型性能存在差异.
结论:
- 人工智能预测模型为预测严重的COVID-19患者预后提供了有希望的功能.
- 在不同人群中模型性能的变化需要谨慎的临床实施.
- 进一步的研究和开发对于提高AI模型对COVID-19的预测准确性和临床实用性至关重要.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.2K
08:20Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
Published on: October 27, 2023
1.4K
相关概念视频
Steps in Outbreak Investigation
115
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
115
Cancer Survival Analysis
334
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
334
Receiver Operating Characteristic Plot
106
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
106
Sensitivity, Specificity, and Predicted Value
228
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
228
