Predicting lung cancer incidence from air pollution exposures using shapelet-based time series analysis

Hong Jun Yoon, Songhua Xu, Georgia Tourassi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages565-568
Number of pages4
ISBN (Electronic)9781509024551
DOIs
StatePublished - Apr 18 2016
Event3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States
Duration: Feb 24 2016Feb 27 2016

Publication series

Name3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016

Other

Other3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
CountryUnited States
CityLas Vegas
Period2/24/162/27/16

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Health Information Management

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