利用人工智能预测和分析推动全球结核病的社会经济,环境和医疗保健因素
Md Siddikur Rahman1, Abu Bokkor Shiddik2
1Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh. siddikur@brur.ac.bd.
Scientific reports
|April 19, 2025
概括
人工智能,特别是XGBoost和可解释AI,可以预测结核病发病率和死亡率. 影响结核病的关键因素包括治疗成功,MDR-TB和空气污染,提供新的公共卫生策略.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 人工智能的人工智能
背景情况:
- 结核病 (TB) 仍然是一个重大的全球卫生挑战,导致大量的死亡率和发病率.
- 众所周知,社会经济,环境和医疗保健因素会影响结核病趋势.
- 预测建模和疾病决定因素的识别对于有效的公共卫生干预至关重要.
研究的目的:
- 使用先进的人工智能 (AI) 预测全球结核病发病率和死亡率.
- 确定影响全球结核病发病率和死亡率的关键决定因素.
- 评估机器学习模型在公共卫生监测中的表现.
主要方法:
- 采用了XGBoost (eXtreme梯度提升) 机器学习模型与XAI (可解释的人工智能) 相结合.
- 利用空间分析,包括莫兰的I,以确定地理集群和决定因素.
- 从2000年到2022年,分析了194个国家的全球结核病数据.
主要成果:
- 治疗成功率和MDR-TB治疗的开始被确定为结核病发病率的关键决定因素.
- 在HIV阳性患者的结核病发病率与整体结核病发病率 (r=0.83) 之间发现了强烈的正相关性.
- 确诊的MDR-TB病例 (SHAP=0.874) 和空气污染 (SHAP=1.36) 显著影响了结核病发病率.
- 该XGBoost模型显示出优异的预测性能,低RMSE (0.88) 和高R2 (0.67).
结论:
- 整合XGBoost和XAI为应对全球结核病提供了一个强大的框架.
- 这种方法提高了结核病发病率和死亡率的预测.
- 先进的分析技术对于在公共卫生和疾病控制方面熟练应用至关重要.
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