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Scientific article
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English

Automatic medical encoding with SNOMED categories

Contributors

Ruch, Patrick; Gobeill, Julien; Lovis, Christian; Geissbuhler, Antoine

Published in

BMC medical informatics and decision making, vol. 8 Suppl 1, S6

Publication date

2008

Abstract

BACKGROUND: In this paper, we describe the design and preliminary evaluation of a new type of tools to speed up the encoding of episodes of care using the SNOMED CT terminology. METHODS: The proposed system can be used either as a search tool to browse the terminology or as a categorization tool to support automatic annotation of textual contents with SNOMED concepts. The general strategy is similar for both tools and is based on the fusion of two complementary retrieval strategies with thesaural resources. The first classification module uses a traditional vector-space retrieval engine which has been fine-tuned for the task, while the second classifier is based on regular variations of the term list. For evaluating the system, we use a sample of MEDLINE. SNOMED CT categories have been restricted to Medical Subject Headings (MeSH) using the SNOMED-MeSH mapping provided by the UMLS (version 2006). RESULTS: Consistent with previous investigations applied on biomedical terminologies, our results show that performances of the hybrid system are significantly improved as compared to each single module. For top returned concepts, a precision at high ranks (P0) of more than 80% is observed. In addition, a manual and qualitative evaluation on a dozen of MEDLINE abstracts suggests that SNOMED CT could represent an improvement compared to existing medical terminologies such as MeSH. CONCLUSION: Although the precision of the SNOMED categorizer seems sufficient to help professional encoders, it is concluded that clinical benchmarks as well as usability studies are needed to assess the impact of our SNOMED encoding method in real settings. AVAILABILITIES : The system is available for research purposes on: http://eagl.unige.ch/SNOCat.

Keywords

  • Algorithms
  • Automation
  • Efficiency, Organizational
  • Episode of Care
  • Forms and Records Control
  • MEDLINE
  • Systematized Nomenclature of Medicine

UNIGE affiliation entities

UNIGE research groups

Citation (ISO format)

RUCH, Patrick et al. Automatic medical encoding with SNOMED categories. In: BMC medical informatics and decision making, 2008, vol. 8 Suppl 1, p. S6. doi: 10.1186/1472-6947-8-S1-S6

Main files (1)

Article (Accepted version)

Identifiers

Additional URL for this publication

http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=19007443

Journal ISSN

1472-6947
884views
480downloads

Technical informations

Creation

31/03/2009 11:45:00

First validation

31/03/2009 11:45:00

Update

14/10/2025 07:13:15

Status update

14/03/2023 15:03:50

Last indexation

03/11/2025 16:31:50
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