Affiliations: Cancer Diagnosis Program, National Cancer Insititute,
National Institutes of Health, Bethesda, MD, USA. E-mail:
bermanj@mail.nih.gov
Abstract: Assigning nomenclature codes to biomedical data is an arduous,
expensive and error-prone task. Data records are coded to to provide a common
representation of contained concepts, allowing facile retrieval of records via
a standard terminology. In the medical field, cancer registrars, nurses,
pathologists, and private clinicians all understand the importance of
annotating medical records with vocabularies that codify the names of diseases,
procedures, billing categories, etc. Molecular biologists need codified medical
records so that they can discover or validate relationships between
experimental data and clinical data. This paper introduces a new approach to retrieving data records
without prior coding. The approach achieves the same result as a search over
pre-coded records. It retrieves all records that contain any terms that are
synonymous with a user's query-term. A recently described fast algorithm (the
doublet method) permits quick iterative searches over every synonym for any
term from any nomenclature occurring in a dataset of any size. As a demonstration, a 105+ Megabyte corpus of
Pubmed abstracts was searched for medical terms. Query terms were matched
against either of two vocabularies and expanded as an array of equivalent
search items. A single search term may have over one hundred nomenclature
synonyms, all of which were searched against the full database. Iterative
searches of a list of concept-equivalent terms involves many more operations
than a single search over pre-annotated concept codes. Nonetheless, the doublet
method achieved fast query response times (0.05 seconds using Snomed and 5
seconds using the Developmental Lineage Classification of Neoplasms, on a
computer with a 2.89 GHz processor). Pre-annotated datasets lose their value when the chosen vocabulary
is replaced by a different vocabulary or by a different version of the same
vocabulary. The doublet method can employ any version of any vocabulary with no
pre-annotation. In many instances, the enormous effort and expense associated
with data annotation can be eliminated by on-the-fly doublet matching. The algorithm for nomenclature-based database searches using the
doublet method is described. Perl scripts for implementing the algorithm and
testing execution speed are provided as open source documents available from
the Association for Pathology Informatics
(www.pathologyinformatics.org/informatics_r.htm).
