Representation of Assertions in Clinical Free Text using SNOMED CT

Background Free-text clinical reports are often unstructured and make it difficult to aggregate information. A standardised representation of useful information in clinical text data is therefore important for facilitating retrieval and analysis. Aims To investigate the feasibility of using Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) as a standard reference terminology for representing medical concepts and their assertions in clinical free text. Method The semantics encoded in SNOMED CT was proposed to automatically represent concepts and assertions in free text. Representations for the encoding of these entities were achieved using SNOMED CT expression templates. Results SNOMED CT expression templates were able to be developed to populate medical concepts and assertion data from the free text. A corpus of 726 clinical free text reports containing 30,518 assertions was used to evaluate the proposed methodology and among these 93.8% of the assertions could be represented using SNOMED CT. Conclusion The SNOMED CT representation of medical concepts and assertions in clinical free text show promise. Frequently occurring assertions such as “present,” “absent” and “possible” can be readily represented, while future work will still need to improve the coverage of other assertions typically found in the free text such as “Conditional” and “Hypothetical”

Representation of assertions in clinical free-­text using\ud SNOMED CT

Information retrieval (IR) by clinicians in the healthcare\ud setting is critical for informing clinical decision-making.\ud However, a large part of this information is in the form\ud of free-text and inhibits clinical decision support and\ud effective healthcare services. This makes meaningful use\ud of clinical free-­text in electronic health records (EHRs)\ud for patient care a difficult task. Within the context of\ud IR, given a repository of free-­text clinical reports, one\ud might want to retrieve and analyse data for patients who\ud have a known clinical finding