Expanding Consumer Health Vocabularies with Frequency-Conserving Internal Context Models

Mujib, Munif Ishad; Yang, Christopher C.; Zhao, Mengnan; Williams, Jake Ryland

doi:10.1109/ichi.2018.00034

Cited by 1 publication

(1 citation statement)

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“…However, our research also indicates that the open source project that has created it is not active and that raises concerns for future support and expansion. Moreover, the source was created with the purpose of helping clinicians to communicate complex health-related concepts to their patients (Mujib et al, 2018) and had no ontological properties. Due to these reasons, we decided not to use CHV as the main source in our next study.…”

Section: Discussionmentioning

confidence: 99%

Utility of SNOMED CT in automated expansion of clinical terms in discharge summaries: Testing issues of coverage

Zivaljevic

Atalag

Warren

2020

HIM J

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Objective: This study tests coverage of SNOMED CT as an expansion source in the process of automated expansion of clinical terms found in discharge summaries. Term expansion is commonly used as a technique in knowledge extraction, query formulation and semantic modelling among other applications. However, characteristics of the sources might affect credibility of outputs, and coverage is one of them. Method: We developed an automated method for testing coverage of more than one source at a time. We used several methods to clean our corpus of discharge summaries before we extracted text fragments as candidates for clinical concepts. We then used Unified Medical Language System (UMLS) sources and UMLS REST API to filter concepts from the pool of text fragments. Statistical measures like true positive rate and false negative rate were used to decide on the coverage of the source. We also tested the coverage of the individual SNOMED CT hierarchies using the same methods. Results: Findings suggest that a combination of four terminologies tested (SNOMED CT, NCI, LNC and MSH) achieves over 90% of coverage for term expansion. We also found that the SNOMED CT hierarchies that hold clinically relevant concepts provided 60% of coverage. Conclusion: We believe that our findings and the method we developed will be of use to both scientists and practitioners working in the domain of knowledge extraction.

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Section: Discussionmentioning

confidence: 99%