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Related Experiment Videos

Bilateral asymmetry prediction.

Ronald Neil Kostoff1

  • 1Office of Naval Research, Arlington, Virginia 22217, USA. kostofr@onr.navy.mil

Medical Hypotheses
|July 31, 2003
PubMed
Summary

This study used text mining of Medline records to predict lateral organ cancer incidence, showing strong agreement with National Cancer Institute data for lung, kidney, testes, and ovary cancers.

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Area of Science:

  • Biomedical Informatics
  • Oncology
  • Medical Data Mining

Background:

  • Lateral asymmetries in organ cancer incidence are not well-established.
  • Existing methods for analyzing incidence data are time-consuming and may not capture subtle trends.

Purpose of the Study:

  • To investigate the feasibility of using text mining on the Medline database to predict lateral organ cancer incidence.
  • To compare Medline-derived incidence ratios with established patient data.

Main Methods:

  • Text mining of Medline case reports focusing on right-sided versus left-sided organ cancers (lung, kidney, testes, ovary).
  • Comparison of the ratio of right-to-left organ cancer articles with patient incidence data from the National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) database (1979-1998).

Main Results:

  • High agreement was found between Medline record ratios and NCI patient incidence data.
  • Agreement ranged from within 3% for lung cancer to within 1% for testes and ovary cancer.

Conclusions:

  • Text mining of Medline is a viable method for predicting lateral cancer incidence asymmetries.
  • This technique has potential applications for studying other diseases and systemic asymmetries.

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