Artificial intelligence-assisted occupational lung disease diagnosis

P. Harber, J. M. McCoy, K. Howard, D. Greer, J. Luo

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

An artificial intelligence expert-based system for facilitating the clinical recognition of occupational and environmental factors in lung disease has been developed in a pilot fashion. It utilizes a knowledge representation scheme to capture relevant clinical knowledge into structures about specific objects (jobs, diseases, etc) and pairwise relations between objects. Quantifiers describe both the closeness of association and risk, as well as the degree of belief in the validity of a fact. An independent inference engine utilizes the knowledge, combining likelihoods and uncertainties to achieve estimates of likelihood factors for specific paths from work to illness. The system creates a series of 'paths,' linking work activities to disease outcomes. One path links a single period of work to a single possible disease outcome. In a preliminary trial, the number of 'paths' from job to possible disease averaged 18 per subject in a general population and averaged 25 per subject in an asthmatic population. Artificial intelligence methods hold promise in the future to facilitate diagnosis in pulmonary and occupational medicine.

Original languageEnglish (US)
Pages (from-to)340-346
Number of pages7
JournalCHEST
Volume100
Issue number2
DOIs
StatePublished - Jan 1 1991

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine
  • Cardiology and Cardiovascular Medicine

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