title: Artificial intelligence-assisted occupational lung disease diagnosis.
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.
authors.affiliation: Department of Medicine, University of California, Los Angeles.
authors.forename: P
authors.initials: P
authors.lastname: Harber
authors.forename: J M
authors.initials: JM
authors.lastname: McCoy
authors.forename: K
authors.initials: K
authors.lastname: Howard
authors.forename: D
authors.initials: D
authors.lastname: Greer
authors.forename: J
authors.initials: J
authors.lastname: Luo
mesh_terms: D001185:Artificial Intelligence; D001249:Asthma / Q000175:diagnosis; D003199:Computer Systems; D016208:Databases, Factual; D005103:Expert Systems; D006801:Humans; D016013:Likelihood Functions; D008171:Lung Diseases / Q000175:diagnosis*; D008838:Microcomputers; D009784:Occupational Diseases / Q000175:diagnosis*; D010865:Pilot Projects; D011336:Probability; D012307:Risk Factors