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基于机器学习的吸烟者COVID-19患者的死亡率预测模型

Ali Sharifi-Kia¹, Azin Nahvijou², Abbas Sheikhtaheri³

¹Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.

BMC medical informatics and decision making

|July 21, 2023

概括

机器学习模型准确地预测了吸烟者的COVID-19死亡率. XGBoost模型在入院和入院后的预测方面都取得了高准确性,有助于患者管理.

科学领域:

医疗信息学医疗信息学
公共卫生公共卫生
机器学习机器学习

背景情况:

全球范围内,COVID-19的流行病压倒了医疗保健系统.
现有的死亡率预测模型可能会在特定亚群体 (如吸烟者) 中表现出偏差.
针对高风险群体,如有吸烟史的COVID-19患者,需要有针对性的模型.

研究的目的:

开发和评估机器学习模型,用于预测有吸烟史的COVID-19患者的住院死亡率.
确定这一特定患者队列中死亡率的关键预测因素.
改善吸烟者COVID-19患者的资源配置和患者管理策略.

主要方法:

这是一项回顾性研究,涉及6个医疗中心 (2020-2022) 的678名COVID-19患者,他们有吸烟史.
使用十倍交叉验证开发多种机器学习模型,结合人口统计,护理水平,生命体征,药物和并发症.
创建两个模型集:一个用于入学预测,另一个用于入学后预测,然后进行概率校准.

主要成果:

吸烟者COVID-19患者的住院死亡率为20.1%.
在入院时表现最好的模型 (XGBoost) 实现了87.5%的准确率和86.2%的F1得分.
最好的入院后模型 (XGBoost) 显示了90.5%的准确性和89.9%的F1得分,活跃吸烟被确定为关键预测因素.

结论:

机器学习模型可以有效预测吸烟的COVID-19患者的死亡率.
这些模型为这种高风险群体提供了增强管理和生存预测的潜力.
这些发现强调了在预测COVID-19死亡率时考虑吸烟状况的重要性.

关键词:

新冠病毒疾病2019年数据挖掘是一种数据挖掘.机器学习是机器学习.死亡率死亡率死亡率预测模型的预测模型.吸烟吸烟吸烟吸烟

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