结合机器学习和动态系统技术,在例行收集的初级医疗记录中早期检测呼吸道疫情
Dérick G F Borges1,2, Eluã R Coutinho1,3,4, Thiago Cerqueira-Silva1,5
1Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fiocruz Bahia, Salvador, Brazil.
BMC medical research methodology
|April 16, 2025
概括
一个新的机器学习模型,MMAING,有效地使用初级医疗保健数据检测早期爆发迹象. 它的性能优于现有的方法,并准确预测了COVID-19浪潮,增强了流行病学监测.
科学领域:
- 流行病学和公共卫生.
- 医疗保健中的人工智能
- 机器学习用于疾病监测和监测.
背景情况:
- 早期发现疫情爆发对于流行病监测和及时应对疾病暴发至关重要.
- 来自初级医疗保健接触的综合征监测数据可以作为呼吸系统疾病发病率的代理数据.
- 分析接触趋势的偏差有助于识别潜在爆发的新兴疾病.
研究的目的:
- 开发和验证使用初级医疗保健接触数据进行早期疫情检测的整体模型.
- 评估人工智能和下一代 (MMAING) 组合混合模型的表现,与已建立的监控方法相比.
- 利用无监督的机器学习和动态系统来加强流行病发病的识别.
主要方法:
- 人工智能和下一代 (MMAING) 组合的混合模型是通过结合无监督机器学习和动态系统概念而开发的.
- 分析了来自巴西27个卫生区域 (2017-2023) 的数据,以检测初级医疗保健遭遇的异常增加.
- 验证涉及比较分析,COVID-19大流行期间的评估,并使用真实和合成数据与EARS C1,C2和C3进行比较.
主要成果:
- MMAING组合在早期发现疫情方面表现出很高的有效性,在合成数据上实现了86%的检测概率和85%的积极预测值.
- 在合成数据上的ROC曲线分析中,MMAING的表现优于EARS C1,C2和C3,并且在真实数据上的表现与EARS C2相当.
- 该模型准确地预测了巴西所有四个COVID-19浪潮的开始,证实了其在现实世界中的适用性.
结论:
- 对初级医疗保健接触的时间序列分析可以产生可靠的早期爆发检测方法.
- 在不同场景中,MMAING团队始终在不同场景中确定了疫情爆发,在性能上超越了既定的参考方法.
- 这种方法为加强流行病学监测和公共卫生准备提供了一个强大的工具.
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