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This content will become publicly available on November 27, 2016

Title: The utility of web mining for epidemiological research: studying the association between parity and cancer risk [Web Mining for Epidemiological Research. Assessing its Utility in Exploring the Association Between Parity and Cancer Risk]

Background: The World Wide Web has emerged as a powerful data source for epidemiological studies related to infectious disease surveillance. However, its potential for cancer-related epidemiological discoveries is largely unexplored. Methods: Using advanced web crawling and tailored information extraction procedures we automatically collected and analyzed the text content of 79,394 online obituary articles published between 1998 and 2014. The collected data included 51,911 cancer (27,330 breast; 9,470 lung; 6,496 pancreatic; 6,342 ovarian; 2,273 colon) and 27,483 non-cancer cases. With the derived information, we replicated a case-control study design to investigate the association between parity and cancer risk. Age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for each cancer type and compared to those reported in large-scale epidemiological studies. Results: Parity was found to be associated with a significantly reduced risk of breast cancer (OR=0.78, 95% CI = 0.75 to 0.82), pancreatic cancer (OR=0.78, 95% CI = 0.72 to 0.83), colon cancer (OR=0.67, 95% CI = 0.60 to 0.74), and ovarian cancer (OR=0.58, 95% CI = 0.54 to 0.62). Marginal association was found for lung cancer prevalence (OR=0.87, 95% CI = 0.81 to 0.92). The linear trend between multi-parity and reduced cancer risk was dramatically more pronounced formore » breast and ovarian cancer than the other cancers included in the analysis. Conclusion: This large web-mining study on parity and cancer risk produced findings very similar to those reported with traditional observational studies. It may be used as a promising strategy to generate study hypotheses for guiding and prioritizing future epidemiological studies.« less
Authors:
 [1] ;  [1] ;  [2] ;  [3]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. New Jersey Inst. of Technology, Newark, NJ (United States)
  3. American Cancer Society, Atlanta, GA (United States)
Publication Date:
OSTI Identifier:
1236580
Grant/Contract Number:
AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
Journal of the American Medical Informatics Association
Additional Journal Information:
Journal Volume: 23; Journal Issue: 3; Journal ID: ISSN 1067-5027
Publisher:
Oxford University Press
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING web mining; cancer; epidemiology