Mobile Crowdsourcing - From Theory to Practice
Mobile Crowdsourcing - From Theory to Practice
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移动众包 - 从理论到实践
DOI：
10.1007/978-3-031-32397-3_15
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Wang J
人口健康监测和建模对于控制和干预非传染性疾病（NCD）的公共卫生业务至关重要。卫生管理者通常通过整合医院就诊记录或在居民样本中进行调查来进行人口健康监测的数据收集，但这两种方法成本高且耗时，导致空间覆盖有限。在我们的日常生活中，嵌入多模态传感器和数字健康应用的设备的激增以前所未有的规模生成数据，提供有关个人健康状况或健康相关背景的有价值的众包信息。在本书的这一章中，我们提出了一个新的愿景，称为健康人群感知和计算（HCSC），它在数据收集，链接，集成，增强和分析的整个生命周期内利用机会主义和众包的数字健康足迹，以实现更智能的非传染性疾病人群健康监测的目标。具体来说，我们自己的案例研究称为压缩人口健康，我们将联合收割机AI技术与HCSC相结合，以实现具有成本效益的公共卫生监测。最后，现有的差距将与未来的研究机会和建议，在这个有趣的和新颖的研究领域进行讨论。
Population health monitoring and modelling is important and fundamental for public health operations for the control and intervention of Non-Communicable Diseases (NCD). Healthcare administrators often perform data collection for population health monitoring either by integrating records of hospital visits or conducting survey among a sample of residents, but both approaches are of high cost and time-consuming, which results in limited spatial coverage. The proliferation of devices embedded with multimodality sensors and digital health applications in our daily lives generates data at an unprecedented scale, providing valuable crowdsourced information about personal health status or health-related context. In this book chapter, we propose a new vision, called Health Crowd Sensing and Computing (HCSC), which leverages opportunistic and crowdsourced digital health footprints within a full lifecycle of data collection, linkage, integration, augmentation, and analytics, to realise the goal of more intelligent population health monitoring for NCD. Specifically, our own case study called Compressive Population Health will be introduced, where we combine AI techniques with HCSC to achieve cost-effective public health monitoring. Finally, existing gaps will be discussed with future research opportunities and proposal in this interesting and novel research area.