提高CNN在COVID-19健康分析中的预测准确度
Tae-Hoon Kim1, Asadi Srinivasulu2, Ravikumar Chinthaginjala3
1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, No. 318, Hangzhou, Zhejiang, China. 323020@zust.edu.cn.
Scientific reports
|August 14, 2025
概括
卷积神经网络 (CNN) 从X射线和患者数据中预测COVID-19结果的准确性很高. 解决数据的局限性和可解释性是可靠的COVID-19医疗分析的关键.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 流行病学 流行病学
背景情况:
- 由于COVID-19的流行,医疗保健需要先进的预测模型.
- 卷积神经网络 (CNN) 提供了分析复杂健康数据的潜力.
研究的目的:
- 通过胸部X射线评估CNN以识别COVID-19感染.
- 用临床和电子健康记录评估CNN来预测COVID-19的严重程度.
主要方法:
- 利用了一个多维数据集,包括人口,临床和放射信息.
- 应用CNN架构来预测患者的预后,并识别感染.
- 使用准确性,精度,F1得分和AUC-ROC指标评估模型性能.
主要成果:
- CNN模型实现了97.2%的准确性,96.8%的精度,97.2%的F1得分和0.987的AUC-ROC.
- 识别的挑战:数据有限/不平衡,模型可解释性,过拟合,数据异质性.
- 建议的策略,如数据增强和转移学习,提高了性能.
结论:
- CNNs是COVID-19检测和预后的有效工具.
- 持续的改进和领域专业知识对于基于CNN的医疗分析至关重要.
- 调查结果指导了管理COVID-19结果的数据驱动方法.
相关概念视频
Steps in Outbreak Investigation
204
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:
204
Improving Translational Accuracy
11.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.9K
Sensitivity, Specificity, and Predicted Value
661
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...
661
Residuals and Least-Squares Property
7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
Issues And Trends In Healthcare Delivery System
5.8K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.8K


