预测建模和确定COVID-19患者死亡风险的关键变量
Olawande Daramola1, Tatenda Duncan Kavu2, Maritha J Kotze3,3
1Department of Information Technology, Cape Peninsula University of Technology, Cape Town, South Africa. daramolaj@cput.ac.za.
Scientific reports
|January 17, 2025
概括
机器学习模型准确地预测了南非COVID-19死亡风险. 深度MLP实现了最佳表现,识别了关键因素,如住院时间和血液凝固,用于风险评估.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 流行病学 流行病学
背景情况:
- 南非面临着相当大的COVID-19负担,需要先进的分析工具.
- 人工智能 (AI),特别是机器学习 (ML),为改善COVID-19患者管理提供了潜力.
- 对非洲的COVID-19人工智能应用存在有限的研究.
研究的目的:
- 评估ML算法 (深度MLP,SVM,XGBoost) 用于预测COVID-19死亡风险.
- 评估交叉验证 (CV) 和主要组件分析 (PCA) 对ML模型性能的影响.
- 用SHAP解释ML预测,以确定关键的死亡风险因素.
主要方法:
- 在第一个COVID-19浪潮期间,对490名ICU患者的154个特征进行了回顾性分析.
- 深度MLP,SVM和XGBoost模型的应用.
- 利用CV,合成少数超样本技术 (SMOTE) 和PCA进行模型优化和评估.
- 采用沙普利增量解释 (SHAP) 来实现模型的解释性.
主要成果:
- 深度MLP表现出优异的性能 (F1=0.92,AUC=0.94) 与CV和SMOTE,没有PCA.
- 确定了死亡风险的关键预测因素:停留时间 (LOS),ICU LOS,ICU的时间,出院状态,D-二次数和血液pH.
- 诸如年龄,Pf比率,热素T,费里丁,通风,CRP和ARDS等因素与症状的严重程度和死亡率相关.
结论:
- 机器学习模型,特别是深度MLP,可以有效预测COVID-19死亡风险.
- 像SHAP这样的可解释性方法突出了风险评估的关键临床变量.
- 这项研究提供了对优化ML模型的见解,用于在非洲环境中预测COVID-19死亡率,帮助临床决策.
相关概念视频
Cancer Survival Analysis
328
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...
328
Steps in Outbreak Investigation
105
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:
105
Assumptions of Survival Analysis
92
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
92
Comparing the Survival Analysis of Two or More Groups
141
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
141
Actuarial Approach
61
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
61
Introduction To Survival Analysis
176
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
176


