Automated Mapping of Clinical Terms into SNOMED-CT. An Application to Codify Procedures in Pathology

Abstract

Clinical terminologies are considered a key technology for capturing clinical data in a precise and standardized manner, which is critical to accurately exchange information among different applications, medical records and decision support systems. An important step to promote the real use of clinical terminologies, such as SNOMED-CT, is to facilitate the process of finding mappings between local terms of medical records and concepts of terminologies. In this paper, we propose a mapping tool to discover text-to-concept mappings in SNOMED-CT. Name-based techniques were combined with a query expansion system to generate alternative search terms, and with a strategy to analyze and take advantage of the semantic relationships of the SNOMED-CT concepts. The developed tool was evaluated and compared to the search services provided by two SNOMED-CT browsers. Our tool automatically mapped clinical terms from a Spanish glossary of procedures in pathology with 88.0% precision and 51.4% recall, providing a substantial improvement of recall (28% and 60%) over other publicly accessible mapping services. The improvements reached by the mapping tool are encouraging. Our results demonstrate the feasibility of accurately mapping clinical glossaries to SNOMED-CT concepts, by means a combination of structural, query expansion and named-based techniques. We have shown that SNOMED-CT is a great source of knowledge to infer synonyms for the medical domain. Results show that an automated query expansion system overcomes the challenge of vocabulary mismatch partially.

Integrating terminologies into standard SQL: a new approach for research on routine data

“…The annotation results outperformed the approaches presented by Allones et al [5] and Shah et al [7] and compared very well to the ones presented by Topfer et al [6], who obtained slightly better results, and Elkin et al [4], whose results were slightly inferior to ours. This comparison of final metrics shows that the employed NLP pipeline and annotation algorithm deliver state-of-the-art results and provide an objective evaluation of the query results.…”

“…Elkin et al analyzed mapping algorithms with the SNOMED (systematized nomenclature of medicine) ontology on chest X-ray reports with excellent results [4]. A similar task for pathology was recently presented by Allones et al but required further improvement [5]. In the German language, a 2015 paper presented by Toepfer et al showed very good results [6].…”

Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

“…In conjunction with medical terminologies, a suitable report structure improves the addressability of particular contents for machines. There are different approaches on mapping the clinical terms occurring in free texts of documentations to medical terminologies, such as SNOMED CT, by using text mining methods (Stenzhorn et al, 2009;Spasic et al, 2005;Allones et al, 2014). Extracting machine-readable facts out of raw text facilitates the electronic exchange of the report information between information technology (IT) systems (Bouhaddou et al, 2008;White and Carolan-Rees, 2013).…”

Integrasi Electronic Medical Record (Emr) Dengan Laboratory Information Systems (Lis) Dan Picture Archiving and Communications System (Pacs)

“…Tujuan utama dari SNOMED CT adalah untuk mendukung rekaman klinis efektif data dengan tujuan meningkatkan perawatan pasien. Ini adalah koleksi terstruktur dari istilah medis yang digunakan secara internasional untuk merekam informasi klinis dan dikodekan dalam komputer secara processable (Allones et al 2014).…”