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机器学习框架用于早期检测由急性软性引起的脊髓灰质炎疫情,监测数据

Honey Gemechu¹, Gelane Biru¹, Eyerusalem Gebremeskel²

¹School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, 378, Ethiopia; Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network (AI4PEP), Toronto, Canada.

|March 10, 2026

概括

使用急性性 (AFP) 监测数据的AI框架增强了早期的脊髓灰质炎病毒检测. CatBoost模型实现了92.22%的准确性,确定了改善脊髓灰质炎根除工作的关键临床指标.

科学领域:

公共卫生公共卫生
传染病流行病学传染病流行病学
人工智能在医学中的应用

背景情况:

脊髓灰质炎的根除正在进行中,但受到疫苗衍生的脊髓灰质炎病毒循环的挑战.
目前的急性性 (AFP) 监测在数据及时性,分析和报告方面面临限制.
人工智能 (AI) 提供了增强早期检测和响应监控系统的机会.

研究的目的:

开发和评估基于人工智能的预测框架,以利用AFP监测数据改进早期发现脊髓灰质炎病例.
为了比较各种机器学习算法和组合用于脊髓灰质炎病毒预测的性能.
通过模型可解释性识别小儿麻病毒感染的关键临床预测因素.

主要方法:

开发了一个AI框架,整合了来自AFP监视的地理,疫苗接种,临床和实验室数据.
训练并比较了十个机器学习算法,包括基于树的,概率的,神经方法和合奏.
利用了莎普利的附加式解释和局部可解释的模型-不可知论的解释来解释模型的可解释性.

主要成果:

基于CatBoost的AI模型表现出卓越的性能,准确率为92.22%,AUC为0.99.
该模型的表现优于单独的机器学习方法和集体机器学习方法.
疑似AFP病例的关键预测因素包括体温,疲劳和喉痛.

结论:

人工智能框架有效地提高了AFP监控系统中的早期脊髓灰质炎病毒检测.
可解释的人工智能集成改善了疫情预测,并支持及时的公共卫生干预.
这种方法加强了全球致力于根除脊髓灰质炎的努力.

关键词:

剧烈的松性麻症剧烈的松性麻症疾病监测疾病监测机器学习机器学习疫情爆发的预测预测.脊髓灰质炎是一种脊髓灰质炎.

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