在泰国,利用机器学习揭开白病风险的驱动因素
Pikkanet Suttirat1,2, Sudarat Chadsuthi3, Charin Modchang1,2,4
1Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, Thailand.
PLoS neglected tropical diseases
|October 14, 2025
概括
机器学习准确地预测了泰国的白病风险,确定了养殖和气候作为关键因素. 在COVID-19期间,模型性能下降,这表明对这种公共卫生威胁的监视挑战.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 莱普陀螺杆菌病在泰国是一个重要的公共卫生问题.
- 疫情受到环境和社会经济因素的影响.
- 预测建模可以帮助管理勒螺旋体风险.
研究的目的:
- 开发和验证XGBoost机器学习模型,用于预测泰国省级白病爆发风险.
- 为了确定关键的气候,社会经济和农业预测因素的白病风险.
- 评估模型在流行病前,流行病前和流行病后期间的表现.
主要方法:
- 采用了2007-2022年国家勒螺杆菌病监测数据.
- 开发了一个XGBoost模型,整合了气候,社会经济和农业数据.
- 从2018-2022年开始,用于因子识别和验证模型性能的SHapley添加物扩展 (SHAP) 分析.
主要成果:
- 在XGBoost模型中,在大流行前 (AUC=0.937) 和大流行后 (AUC=0.951) 期间,对白病风险的预测准确度很高.
- 关键预测因素包括大米生产,家庭规模和气候变量.
- 在COVID-19大流行期间 (2020-2021) 模型性能下降,这表明监控影响.
结论:
- 在泰国,机器学习对预测白病风险有效.
- 环境和社会经济因素显著影响疫情爆发风险.
- 该模型为早期预警系统和针对这一被忽视的热带疾病的有针对性的干预提供了基础.
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