基于机器学习的COVID-19预后预测,使用临床和血液学数据
Fatemah O Kamel1, Rania Magadmi1, Sulafah Qutub2
1Department of Clinical Pharmacology, King Abdulaziz University Faculty of Medicine, Jeddah, SAU.
Cureus
|December 13, 2023
概括
机器学习使用临床数据准确预测COVID-19患者的结果和严重程度. 像中性粒细胞和D-二次体这样的血液学参数是疾病进展和预后的关键预测因素.
科学领域:
- 医疗信息学 医疗信息学
- 血液学 血液学 血液学
- 传染性疾病 传染性疾病
背景情况:
- 随着COVID-19的流行,全球卫生保健面临着重大挑战.
- 准确的预后预测对于管理COVID-19患者护理和资源分配至关重要.
研究的目的:
- 评估机器学习模型在预测COVID-19患者结果和疾病严重程度方面的有效性.
- 确定主要的临床和血液学参数,作为COVID-19预后的预测指标.
主要方法:
- 一项多中心的回顾性研究,涉及485名COVID-19患者.
- 对人口统计数据,症状,血液学变量,治疗方法和临床结果的分析.
- 机器学习算法的应用和比较:随机森林,多层感知子和支持矢量机器.
主要成果:
- 机器学习模型在预测疾病严重程度和临床结果方面表现出很高的表现,实现曲线下的面积 (AUC) 为0.96.
- 血液学参数,特别是中性粒细胞,淋巴细胞,D-二次体和单细胞,被确定为最重要的预测因素.
- 所有评估的机器学习方法都表现出可比的预测能力.
结论:
- 机器学习技术对于预测COVID-19患者的结果和严重程度是可行的和有效的.
- 血液学标记是评估COVID-19预后和患者结果的关键指标.
- 这项研究强调了利用例行收集的数据来改善COVID-19患者管理的潜力.
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