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使用人工智能驱动的热点映射用于尼日利亚西南部结核病例发现的有效性

Abiola Alege¹, Sumbul Hashmi², Rupert Eneogu³

¹Society for Family Health, 8, Port Harcourt Crescent, Area 11, Garki, Abuja 900247, Federal Capital Territory, Nigeria.

Tropical medicine and infectious disease

|May 24, 2024

概括

一个新的贝叶斯模型准确地确定了尼日利亚高风险结核病 (TB) 地区. 这种方法显著改善了活跃病例发现 (ACF),通过将资源转移到有较高未诊断结核病率的社区.

科学领域:

流行病学流行病学
公共卫生公共卫生
地理信息系统 (GIS) 是指地理信息系统.

背景情况:

尼日利亚在确诊的结核病例和估计的结核病例之间存在很大的差距.
创新策略对于识别结核病传播热点和实施有针对性的活跃病例发现 (ACF) 至关重要.
社区层面的数据与社会人口统计信息相结合,可以揭示局部高风险地区.

研究的目的:

评估一个社区级别的贝叶斯预测模型,用于识别未被诊断的结核病例.
评估这种方法是否提高了积极病例发现 (ACF) 干预措施的效率.

主要方法:

对尼日利亚西南部四个州的ACF数据进行了回顾性分析.
将行政区分为较小的人口集群,并绘制结核病查结果的地图.
将ACF数据与开源上下文数据结合起来,以训练贝叶斯推理模型来预测社区一级的结核病阳性率.
在地理门户网站上可视化预测,以指导ACF干预,并将产量与传统的通知数据进行比较.

主要成果:

贝叶斯模型预测的高TB阳性集群的产量比其他地点高1.75倍 (p < 0.001).
该模型有效地识别了具有更高未诊断结核病率的社区.

结论:

社区级别的贝叶斯预测建模在指导ACF实施者到高结核病阳性地区方面表现有前途.
这种方法可以显著提高发现未被诊断的结核病的效率,并弥合诊断差距.

关键词:

人工智能的人工智能是人工智能.热点是热点的热点.绘制地图,绘制地图.模拟建模的模型.结核病是一种肺结核病.

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