Quality-Guaranteed and Cost-Effective Population Health Profiling: A Deep Active Learning Approach
Quality-Guaranteed and Cost-Effective Population Health Profiling: A Deep Active Learning Approach
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DOI：
10.1145/3617179
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发表时间：
2023-08
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通讯作者：
Long Chen;Jiangtao Wang;P. Thakuriah
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作者：
Long Chen;Jiangtao Wang;P. Thakuriah
可靠性和成本是分析多种非传染性疾病人口规模患病率的两个主要考虑因素。在本文中，我们利用不同的传统感知区域（TS-A）的疾病内和疾病间的相关性，以减少所需的分析任务的数量，而不影响数据的可靠性。具体来说，我们提出了一种名为压缩人口健康TS-A选择（CPH-TS）的新方法，该方法将最先进的配置文件推断，数据增强和主动学习融合在统一的深度学习框架中。该算法能够在每个剖析周期中主动选择最少数量的TS-A区域进行剖析任务分配，同时在概率可靠性保证的情况下推断未剖析区域上的缺失数据。我们评估了我们的方法对现实世界的患病率数据集的伦敦，这表明CPH-TS的有效性。一般来说，CPH-TS分配的任务比基线少11.1-27.3%，仅将任务分配给34.7%的子区域，而分析误差在95%的周期中低于5%。
Reliability and cost are two primary considerations for profiling population-scale prevalence (PPP) of multiple non-communicable diseases (NCDs). In this paper, we exploit intra-disease and inter-disease correlation in different traditionally-sensed-areas (TS-A) to reduce the number of profiling tasks required without compromising data reliability. Specifically, we propose a novel approach called Compressive Population Health TS-A Selection (CPH-TS), which blends the state-of-the-art profile inference, data augmentation and active learning in a unified deep learning framework. It can actively select the minimum number of TS-A regions for profiling task allocation in each profiling cycle, while deducing the missing data on the unprofiled regions with a probabilistic guarantee of reliability. We evaluate our approach on real-world prevalence datasets of London, which shows the effectiveness of CPH-TS. In general, CPH-TS assigned 11.1-27.3% fewer tasks than baselines, assigning tasks to only 34.7% of the sub-regions while the profiling error was below 5% for 95% of the cycles.