在流行病监测数据中检测异常,使用机器学习技术
Peter U Eze1, Nicholas Geard1, Ivo Mueller2
1School of Computing and Information Systems, The University of Melbourne, Parkville, VIC 3010, Australia.
Healthcare (Basel, Switzerland)
|July 14, 2023
概括
机器学习异常检测可以通过识别早期爆发信号来改善疾病监测. 这种方法有助于及时干预疟疾等疾病,即使使用大型数据集.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 公共卫生监督 公共卫生监督
背景情况:
- 疾病监测数据在实时决策中未得到充分利用.
- 早期发现疫情和干预优先事项对于疾病控制至关重要.
研究的目的:
- 探索无监督异常检测机器学习技术,以发现流行病学信号.
- 评估这些方法在改善疾病监测方面的潜力,以巴西亚马逊疟疾数据集作为案例研究.
主要方法:
- 将无监督异常检测机器学习模型应用于巴西亚马逊疟疾监测数据集.
- 评估模型检测疫情发作,峰值和阳性病例比例变化的能力.
- 确定最佳的模型数量 (top-k) 以最大限度地检测不同健康区域的异常.
主要成果:
- 机器学习模型成功地提供了2016年疟疾爆发发病,峰值和变化点的早期迹象.
- 没有一个单一的模型能够检测到所有健康区域的所有异常,这凸显了综合方法的必要性.
- 主要组件分析,随机异常值选择和最小协差决定因素被确定为最大化异常检测覆盖范围的前三种模型.
结论:
- 异常检测是一种有价值的方法,用于识别大型时空数据集中的流行病学模式.
- 建议的方法可以复制到其他疾病和地点,以加强监测和及时干预.
- 这种方法支持通过改善监测和数据利用来控制特有病的努力.
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