论文标题
Covid-Net Biochem：建立机器学习模型的解释性驱动框架，用于预测临床和生物化学数据的COVID-19患者的生存和肾脏损伤
COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data
论文作者
论文摘要
自从世界卫生组织在2020年宣布Covid-19 A Pandemic以来，全球社区在控制和减轻SARS-COV-2病毒的传播方面面临着持续的挑战，及其不断发展的子变量和重组者。大流行期间的一个重大挑战不仅是对阳性病例的准确检测，而且还有效地预测了与并发症和患者生存概率相关的风险。这些任务需要大量的临床资源分配和关注。在这项研究中，我们介绍了Covid-Net Biochem，这是一个用于构建机器学习模型的多功能且可解释的框架。我们将此框架应用来预测COVID-19患者的生存和住院期间急性肾脏损伤的可能性，在透明的系统方法中利用临床和生化数据。提出的方法通过将域专业知识与解释性工具无缝整合到机器学习模型设计，从而使模型决策能够基于关键的生物标志物。这促进了专门针对医疗应用的机器制造的更透明和可解释的决策过程。
Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SARS-CoV-2 virus, as well as its evolving subvariants and recombinants. A significant challenge during the pandemic has not only been the accurate detection of positive cases but also the efficient prediction of risks associated with complications and patient survival probabilities. These tasks entail considerable clinical resource allocation and attention.In this study, we introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization, utilizing clinical and biochemical data in a transparent, systematic approach. The proposed approach advances machine learning model design by seamlessly integrating domain expertise with explainability tools, enabling model decisions to be based on key biomarkers. This fosters a more transparent and interpretable decision-making process made by machines specifically for medical applications.
