Symbolic rule-based classification of lung cancer stages from free-text pathology reports

Objective To classify automatically lung tumorenodeemetastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. Design By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicineeclinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines t