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

ABSTRACT:

Objective

To classify automatically lung tumor-node-metastases (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 medicine-clinical 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 to calculate the TNM stage.

Measurements

The accuracy measure and confusion matrices were used to evaluate the T

SUBMITTER: Nguyen AN 

PROVIDER: S-EPMC2995652 | biostudies-literature | 2010 Jul-Aug

REPOSITORIES: biostudies-literature