开发一种机器学习模型,以预测COVID-19相关的粘膜菌病的发展风险
Rajashri Patil1, Sahjid Mukhida1, Jyoti Ajagunde1
1Department of Microbiology, Dr DY Patil Medical College Hospital & Research Centre, Dr DY Patil Vidyapeeth, Pimpri, Pune 18, India.
Future microbiology
|January 31, 2024
概括
这项研究确定了COVID-19相关的粘膜菌 (CAM) 的风险因素,并开发了一种机器学习模型来预测易感性. 该模型准确地预测了CAM的发展,有助于对真菌感染的早期干预.
科学领域:
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
- 眼科医生 眼科 眼科
背景情况:
- 与COVID-19相关的粘真菌菌 (CAM) 是一种严重的真菌感染,死亡率高.
- 确定CAM的预测因素对于及时诊断和治疗至关重要.
- 现有的诊断方法可能无法有效地捕捉所有处于风险的患者.
研究的目的:
- 为了确定与犀牛轨道脑粘膜相关的定量风险因素.
- 开发和验证用于预测CAM易感性的机器学习模型.
- 评估各种感染预测算法的实用性.
主要方法:
- 对124名疑似CAM患者的临床病理学数据的分析.
- 统计量化患者因素与CAM之间的关联.
- 开发和测试用于风险预测的机器学习模型.
主要成果:
- 糖尿病,非侵入性通风和高血压与CAM有显著的关联.
- 机器学习模型在预测CAM概率方面表现出很高的准确性.
- 发现了与放射学证实的CAM病例具有统计学意义的关联.
结论:
- 机器学习为预测CAM开发提供了一个强大的工具.
- 这种方法可以适应创建各种感染和并发症的预测算法.
- 准确的预测有助于积极管理机会性感染.
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