使用人工智能评估气候对儿童病毒性呼吸道疾病的影响
Mikhail I Krivonosov1, Ekaterina Pazukhina2, Alexey Zaikin3,4
1Research Center in Artificial Intelligence, Lobachevsky State University, 603022 Nizhny Novgorod, Russia.
Journal of clinical medicine
|December 17, 2024
概括
气候数据略有改善了用于预测儿童严重呼吸道病毒感染 (RVI) 的后勤回归模型. 温度,露点和湿度是关键因素,尽管对机器学习算法的影响有所不同.
科学领域:
- 环境健康 环境健康
- 儿童传染病 儿童传染病
- 气候变化对健康的影响
背景情况:
- 呼吸道病毒感染 (RVI) 呈现季节性趋势,受环境和气候因素的影响.
- 温度和湿度等天气变量会影响液滴传播病毒的传播和疾病的严重程度.
- 气候在儿科RVI并发症中的预测作用,特别是极端天气,尚不清楚.
研究的目的:
- 研究气候变量对预测儿科呼吸道病毒感染的严重程度的影响.
- 将机器学习模型的预测性能与气候数据和没有气候数据进行比较.
主要方法:
- 在罗马 (2018-2023) 为下呼吸道感染住院的1610例儿科住院病例 (0-18岁) 的回顾性队列研究.
- 病毒病原体通过鼻喉分子测试确定.
- 机器学习模型 (逻辑回归,随机森林) 使用人口统计,临床和前一周的天气数据进行评估.
主要成果:
- 在将天气变量纳入时,物流回归模型显示了边际准确度的增加 (0.785到0.793).
- 平均温度,露点和湿度是后勤回归模型中重要的预测因素.
- 其他机器学习算法与气候数据没有显示类似的预测改进.
结论:
- 气候变量可以适度增强用于预测儿童RVI严重性的后勤回归模型.
- 不同算法的不一致性能凸显了将环境数据集成到临床预测模型中的挑战.
- 需要进一步的研究,以开发可靠的模型,用于医疗保健环境中的临床应用.
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