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In this chapter, we provide a quantitative evaluation on how the quality of the SNOMED CT subtype hierarchy directly affects the effectiveness of patient cohort queries. Using a de-identified COVID-19 Electronic Health RecordElectronic Health Records (EHRs) dataset [162], licensed by the University of Texas Health Science Center at Houston, we assess the impact of inaccurate and missing is-a relationships in SNOMED CT on the precision and recall of patient cohort queries, respectively.

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Impact of Hierarchical Inconsistencies

  • 章节

  • 发表日期：

    2026年

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  • Guo-Qiang Zhang,
  • Rashmie Abeysinghe,
  • Licong Cui

摘要

In this chapter, we provide a quantitative evaluation on how the quality of the SNOMED CT subtype hierarchy directly affects the effectiveness of patient cohort queries. Using a de-identified COVID-19 Electronic Health RecordElectronic Health Records (EHRs) dataset [162], licensed by the University of Texas Health Science Center at Houston, we assess the impact of inaccurate and missing is-a relationships in SNOMED CT on the precision and recall of patient cohort queries, respectively.