Abstract：
Aim: The study aimed to identify quantitative parameters that increase the risk of rhino-orbito-cerebral mucormycosis, and subsequently developed a machine learning model that can anticipate susceptibility to developing this condition. Methods: Clinicopathological data from 124 patients were used to quantify their association with COVID-19-associated mucormycosis (CAM) and subsequently develop a machine learning model to predict its likelihood. Results: Diabetes mellitus, noninvasive ventilation and hypertension were found to have statistically significant associations with radiologically confirmed CAM cases. Conclusion: Machine learning models can be used to accurately predict the likelihood of development of CAM, and this methodology can be used in creating prediction algorithms of a wide variety of infections and complications.
Fungal infections caused by the Mucorales order of fungi usually target patients with a weakened immune system. They are usually also associated with abnormal blood sugar states, such as in diabetic patients. Recent work during the COVID-19 outbreak suggested that excessive steroid use and diabetes may be behind the rise in fungal infections caused by Mucorales, known as mucormycosis, in India, but little work has been done to see whether we can predict the risk of mucormycosis. This study found that these fungal infections need not necessarily be caused by Mucorales\' species, but by a wide variety of fungi that target patients with weak immune systems. Secondly, we found that diabetes, breathing-assisting devices and high blood pressure states had associations with COVID-19-associated fungal infections. Finally, we were able to develop a machine learning model that showed high accuracy when predicting the risk of development of these fungal infections.
Fungal infections caused by the Mucorales order of fungi usually target patients with a weakened immune system. They are usually also associated with abnormal blood sugar states, such as in diabetic patients. Recent work during the COVID-19 outbreak suggested that excessive steroid use and diabetes may be behind the rise in fungal infections caused by Mucorales, known as mucormycosis, in India, but little work has been done to see whether we can predict the risk of mucormycosis. This study found that these fungal infections need not necessarily be caused by Mucorales\' species, but by a wide variety of fungi that target patients with weak immune systems. Secondly, we found that diabetes, breathing-assisting devices and high blood pressure states had associations with COVID-19-associated fungal infections. Finally, we were able to develop a machine learning model that showed high accuracy when predicting the risk of development of these fungal infections.
摘要：
目的：该研究旨在确定增加犀牛或大脑毛霉菌病风险的定量参数，并随后开发了一种机器学习模型，可以预测这种情况的发生。方法：使用来自124例患者的临床病理数据来量化其与COVID-19相关的毛霉菌病（CAM）的关联，并随后开发机器学习模型来预测其可能性。结果：糖尿病，研究发现,无创通气和高血压与放射学证实的CAM病例有统计学显著关联.结论：机器学习模型可用于准确预测CAM发展的可能性，这种方法可用于创建各种感染和并发症的预测算法。
由真菌的Mucorales顺序引起的真菌感染通常针对免疫系统减弱的患者。它们通常也与异常的血糖状态有关，如糖尿病患者。最近在COVID-19爆发期间的工作表明，过度使用类固醇和糖尿病可能是由Mucorales引起的真菌感染增加的原因，被称为毛霉菌病，在印度,但是我们几乎没有做什么工作来研究我们是否可以预测毛霉菌病的风险。这项研究发现，这些真菌感染不一定是由Mucorales物种引起的，而是针对免疫系统较弱的患者的各种真菌。其次,我们发现糖尿病，呼吸辅助装置和高血压状态与COVID-19相关真菌感染有关.最后,我们能够开发出一种机器学习模型，该模型在预测这些真菌感染的发展风险时显示出很高的准确性。
由真菌的Mucorales顺序引起的真菌感染通常针对免疫系统减弱的患者。它们通常也与异常的血糖状态有关，如糖尿病患者。最近在COVID-19爆发期间的工作表明，过度使用类固醇和糖尿病可能是由Mucorales引起的真菌感染增加的原因，被称为毛霉菌病，在印度,但是我们几乎没有做什么工作来研究我们是否可以预测毛霉菌病的风险。这项研究发现，这些真菌感染不一定是由Mucorales物种引起的，而是针对免疫系统较弱的患者的各种真菌。其次,我们发现糖尿病，呼吸辅助装置和高血压状态与COVID-19相关真菌感染有关.最后,我们能够开发出一种机器学习模型，该模型在预测这些真菌感染的发展风险时显示出很高的准确性。
