Compressive Population Health: Cost-Effective Profiling of Prevalence for Multiple Non-Communicable Diseases via Health Data Science

压缩人口健康：通过健康数据科学对多种非传染性疾病的患病率进行经济有效的分析

• 批准号: EP/V043544/1
• 负责人: Jiangtao Wang
• 金额: $ 28.78万
• 依托单位: Coventry University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV043544%2F1
• 关键词: Compressive; Population; Health; Cost; Effective

With a growing ageing population and changes in lifestyles, non-communicable diseases (NCD), e.g. heart disease, diabetes, and cancer, have become extremely prevalent in our society, and the situation is more challenging in UK compared to other developed countries. Population health monitoring is fundamental block for public health services, and profiling population-scale prevalence of multiple NCD across different regions (e.g., building the spatially fine-grained morbidity rate map) is one of the most important tasks. However, traditional public health data collection and prevalence profiling approaches, such as clinic-visit-based data integration and health surveys, are often very costly and time-consuming. This project proposes a novel paradigm, called compressive population health (CPH for short), to reduce the data collection cost during the profiling of prevalence to the maximum extent. The basic idea CPH is that a subset of areas is intelligently selected for data collection and population health profiling in the traditional way, while leveraging inherent data correlations to perform data inference for the rest of the areas. CPH is facilitated by the exploitation of the following types of inherent data correlations found by epidemiologists. (a) Intra-Disease Spatial Correlations. That is, regions are more similar in the prevalence rate of some diseases when they are neighbouring, or share certain common environmental, socioeconomic, and demographical attributes. (b) Inter-Disease Correlations. Multimorbidity, commonly defined as the co-presence of two or more chronic conditions, demonstrates that statistics for different types of disease may also correlate with each other. For example, regions with higher obesity rate are more likely to have higher rates of heart disease and cancers. In order to realize this idea, this project develops three technical work packages to accomplish the following technical goals: (1) Investigate and extract latent data correlations and further utilize them to build learning models for prevalence inference on the target geographical grids. (2) Design intelligent algorithms for selecting traditional-sensed areas for each disease with multi-objective optimization goals including cost, reliability, and latency. (3) Evaluate and interpret the inference results of prevalence rate to ensure the reliability and robustness of the approach. The proposed CPH is a novel solution to a public health data collection challenge enabled by data science and artificial intelligence. It opens the door for a disruptive population health monitoring paradigm with potential significant cost reductions for public health authorities. By closely working with partners from public health sector, including NHS England and Public Health at Warwickshire County Council, we will evaluate the feasibility of this approach based on multiple public health datasets together with relevant demographic/geographic statistics in the same regions.

随着人口老龄化和生活方式的变化，非传染性疾病（NCD），如心脏病，糖尿病和癌症，在我们的社会中变得非常普遍，与其他发达国家相比，英国的情况更具挑战性。人口健康监测是公共卫生服务的基本模块，并在不同地区（例如，建立空间上细粒度的发病率地图）是最重要的任务之一。然而，传统的公共卫生数据收集和流行情况分析方法，如基于诊所访问的数据整合和健康调查，往往非常昂贵和耗时。本项目提出了一种新的模式，称为压缩人口健康（简称CPH），以减少数据收集成本在流行病学的剖析在最大程度上。CPH的基本思想是，以传统方式智能地选择一个区域子集进行数据收集和人口健康分析，同时利用固有的数据相关性对其余区域进行数据推断。利用流行病学家发现的以下类型的固有数据相关性促进了CPH。(a)疾病内部空间相关性。也就是说，当区域相邻或具有某些共同的环境、社会经济和人口特征时，它们在某些疾病的流行率方面更相似。(b)疾病间的相关性。多发性硬化症，通常定义为两种或多种慢性疾病的共存，表明不同类型疾病的统计数据也可能相互关联。例如，肥胖率较高的地区更有可能有较高的心脏病和癌症发病率。为了实现这一想法，本项目开发了三个技术工作包，以实现以下技术目标：（1）调查和提取潜在的数据相关性，并进一步利用它们来建立学习模型，用于在目标地理网格上进行流行推断。(2)设计智能算法，为每种疾病选择传统感知区域，并实现多目标优化目标，包括成本、可靠性和延迟。(3)对患病率的推断结果进行评估和解释，以确保该方法的可靠性和稳健性。拟议的CPH是数据科学和人工智能支持的公共卫生数据收集挑战的新解决方案。它为颠覆性的人口健康监测模式打开了大门，可能会大大降低公共卫生当局的成本。通过与公共卫生部门的合作伙伴密切合作，包括NHS英格兰和沃里克郡理事会的公共卫生，我们将根据多个公共卫生数据集以及相同地区的相关人口/地理统计数据评估这种方法的可行性。

项目成果

Quality-Guaranteed and Cost-Effective Population Health Profiling: A Deep Active Learning Approach

• DOI: 10.1145/3617179
• 发表时间: 2023-08
• 期刊: ACM Transactions on Computing for Healthcare
• 作者: Long Chen;Jiangtao Wang;P. Thakuriah

Completing Missing Prevalence Rates for Multiple Chronic Diseases by Jointly Leveraging Both Intra- and Inter-Disease Population Health Data Correlations

• DOI: 10.1145/3442381.3449811
• 发表时间: 2021-04
• 期刊: Proceedings of the Web Conference 2021
• 作者: Yujie Feng;Jiangtao Wang;Yasha Wang;A. Helal

Mobile Crowdsourcing - From Theory to Practice

移动众包 - 从理论到实践

• DOI: 10.1007/978-3-031-32397-3_15
• 发表时间: 2023
• 作者: Wang J

Deep Compressed Sensing based Data Imputation for Urban Environmental Monitoring

• DOI: 10.1145/3599236
• 发表时间: 2023-10
• 期刊: ACM Transactions on Sensor Networks
• 影响因子: 4.1
• 作者: Qingyi Chang;Dan Tao;Jiangtao Wang;Ruipeng Gao

Human-in-the-loop machine learning with applications for population health

• DOI: 10.1007/s42486-022-00115-4
• 发表时间: 2022-12
• 期刊: CCF Transactions on Pervasive Computing and Interaction
• 影响因子: 2.1
• 作者: Long Chen;Jiangtao Wang;Bin Guo;Liming Chen