Experiments in concept modeling for radiographic image reports

Abstract

Objective: Development of methods for building concept models to support structured data entry and image retrieval in chest radiography.

Design: An organizing model for chest-radiographic reporting was built by analyzing manually a set of natural-language chest-radiograph reports. During model building, clinician-informaticians judged alternative conceptual structures according to four criteria: content of clinically relevant detail, provision for semantic constraints, provision for canonical forms, and simplicity. The organizing model was applied in representing three sample reports in their entirety. To explore the potential for automatic model discovery, the representation of one sample report was compared with the noun phrases derived from the same report by the CLARIT natural-language processing system.

Results: The organizing model for chest-radiographic reporting consists of 62 concept types and 17 relations, arranged in an inheritance network. The broadest types in the model include finding, anatomic locus, procedure, attribute, and status. Diagnoses are modeled as a subtype of finding. Representing three sample reports in their entirety added 79 narrower concept types. Some CLARIT noun phrases suggested valid associations among subtypes of finding, status, and anatomic locus.

Conclusions: A manual modeling process utilizing explicitly stated criteria for making modeling decisions produced an organizing model that showed consistency in early testing. A combination of top-down and bottom-up modeling was required. Natural-language processing may inform model building, but algorithms that would replace manual modeling were not discovered. Further progress in modeling will require methods for objective model evaluation and tools for formalizing the model-building process.