Predicting Lung Cancer Incidence from Air Pollution Exposures Using Shapelet-based Time Series Analysis.
Predicting Lung Cancer Incidence from Air Pollution Exposures Using Shapelet-based Time Series Analysis.
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DOI：
10.1109/bhi.2016.7455960
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发表时间：
2016-02
期刊：
影响因子：
--
通讯作者：
Tourassi G
中科院分区：
文献类型：
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作者：
Yoon HJ;Xu S;Tourassi G
在本文中，我们调查是否肺癌发病率的地理变化可以预测通过检查颗粒物空气污染水平的时空趋势。采用基于shapelet的时间序列分析技术，分析了大气污染水平的区域变化趋势。首先，我们通过国家癌症研究所提供的国家癌症概况确定了2008年至2012年期间报告肺癌发病率高和低的美国县。然后，我们收集的颗粒物暴露水平（PM2.5和PM10）的县在过去的十年（1998-2007年）通过由环境保护局提供的AirData数据集。使用基于shapelet的时间序列模式挖掘，区域环境暴露配置文件进行了检查，以确定频繁发生的连续暴露模式。最后，设计了一个二元分类器来预测美国地区是否预计会根据该地区十年前的PM2.5和PM10暴露情况经历高肺癌发病率。这项研究证实了长期接触PM与肺癌风险之间的联系。此外，研究结果表明，不仅累积暴露水平，而且PM暴露的时间变异性也会影响肺癌风险。
In this paper we investigated whether the geographical variation of lung cancer incidence can be predicted through examining the spatiotemporal trend of particulate matter air pollution levels. Regional trends of air pollution levels were analyzed by a novel shapelet-based time series analysis technique. First, we identified U.S. counties with reportedly high and low lung cancer incidence between 2008 and 2012 via the State Cancer Profiles provided by the National Cancer Institute. Then, we collected particulate matter exposure levels (PM2.5 and PM10) of the counties for the previous decade (1998–2007) via the AirData dataset provided by the Environmental Protection Agency. Using shapelet-based time series pattern mining, regional environmental exposure profiles were examined to identify frequently occurring sequential exposure patterns. Finally, a binary classifier was designed to predict whether a U.S. region is expected to experience high lung cancer incidence based on the region’s PM2.5 and PM10 exposure the decade prior. The study confirmed the association between prolonged PM exposure and lung cancer risk. In addition, the study findings suggest that not only cumulative exposure levels but also the temporal variability of PM exposure influence lung cancer risk.