Cateye: A Hint-Enabled Search Engine Framework for Biomedical Classification Systems

Abstract

Objective: We propose Cateye, a Python-based search engine framework tailored for searching in biomedical classification systems such as ICD-10, DSM-5, MeSH, and SNOMED CT. Many of the biomedical classification systems have coarse-grained and fine-grained structures to handle the different levels of information. The general-purpose search engines, which designed for document retrieval, face three major problems: too strict terminology, not efficient search, and uncertainty to stop searching. These disadvantages make it painful to search in the classification systems.

Materials and Methods: We used the ICD-10 coding systems as our sample materials. We design a hint bar which shown along with the search results and dramatically helps the users to formulate the correct query. A hint is a suggestion of search term which can best divide the search space into half-and-half.

Results: The case studies show that our hint mechanism performs at least one step deeper per search step in most cases.

Conclusion: The source code of Cateye for searching the classification systems associated with coarse-grained and fine-grained architecture is available at https://github.com/jeroyang/cateye.