A pilot study of contextual UMLS indexing to improve the precision of concept-based representation in XML-structured clinical radiology reports.

Despite the advantages of structured data entry, much of the patient record is still stored as unstructured or semistructured narrative text. The issue of representing clinical document content remains problematic. The authors' prior work using an automated UMLS document indexing system has been encouraging but has been affected by the generally low indexing precision of such systems. In an effort to improve precision, the authors have developed a context-sensitive document indexing model to calculate the optimal subset of UMLS source vocabularies used to index each document section. This pilot study was performed to evaluate the utility of this indexing approach on a set of clinical radiology reports. A set of clinical radiology reports that had been indexed manually using UMLS concept descriptors was indexed automatically by the SAPHIRE indexing engine. Using the data generated by this process the authors developed a system that simulated indexing, at the document section level, of the same document set using many permutations of a subset of the UMLS constituent vocabularies. The precision and recall scores generated by simulated indexing for each permutation of two or three UMLS constituent vocabularies were determined. While there was considerable variation in precision and recall values across the different subtypes of radiology reports, the overall effect of this indexing strategy using the best combination of two or three UMLS constituent vocabularies was an improvement in precision without significant impact of recall. In this pilot study a contextual indexing strategy improved overall precision in a set of clinical radiology reports.