对新出现的呼吸系统疾病进行人工智能驱动的健康分析:使用COVID-19数据对也门患者的案例研究
Saleh I Alzahrani1, Wael M S Yafooz2, Ibrahim A Aljamaan1
1Biomedical Engineering Department, College of Engineering, Imam Abdulrahman Bin Faisal University, PO box 1982, Dammam 31451, Saudi Arabia.
Mathematical biosciences and engineering : MBE
|March 14, 2025
概括
人工智能模型可以预测资源有限的环境中的呼吸道疾病趋势. 人工智能准确识别了COVID-19严重程度指标,帮助医疗保健准备.
科学领域:
- * 计算机流行病学
- * 医疗信息学 医疗信息学
- * 预测建模 * 预测建模
背景情况:
- *在低收入国家,由于类似的症状和并发症,区分COVID-19和其他呼吸道疾病是很困难的.
- *由于急性并发症,也门面临的额外挑战是区分COVID-19与其他传染病.
- * 提高医疗保健准备能力需要准确预测呼吸系统疾病趋势.
研究的目的:
- * 探索使用人工智能驱动的预测模型和分类器来预测呼吸系统疾病趋势.
- * 开发数学和机器学习模型,用于预测每天确诊的COVID-19死亡.
- *使用机器学习和深度学习分类器识别COVID-19严重性指标.
主要方法:
- * 开发了自回归 (AR),移动平均 (MA) 和ARMA模型用于时间序列预测.
- *采用机器学习 (ML) 和深度学习 (DL) 算法进行预测分析.
- *对80%的数据进行了统计模型训练,对20%的数据进行了测试;使用了八个ML分类器和DL模型来识别严重程度指标.
主要成果:
- * ARMA模型在预测每日确诊死亡人数方面表现有希望.
- * 在ML分类器中,决策树 (DT) 实现了74.70%的准确性,随机森林 (RF) 实现了74.66%.
- * 核心支持矢量机 (SVM) 在AUC-ROC (71%) 中表现强,用于识别严重性指标.
结论:
- *人工智能驱动的健康分析具有优化医疗保健资源配置的巨大潜力.
- *预测模型可以提高呼吸道疾病趋势的预测,特别是在资源有限的环境中.
- *机器学习和深度学习方法为识别疾病严重程度指标提供了有价值的工具.
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