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Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets

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Mar 22, 2021 version files 36.42 KB

Abstract

Objective: Normalizing mentions of medical concepts to standardized vocabularies is a fundamental component of clinical text analysis. Ambiguity—words or phrases that may refer to different concepts—has been extensively researched as part of information extraction from biomedical literature, but less is known about the types and frequency of ambiguity in clinical text. This study characterizes the distribution and distinct types of ambiguity exhibited by benchmark clinical concept normalization datasets, in order to identify directions for advancing medical concept normalization research.

Materials and Methods: We identified ambiguous strings in datasets derived from the two available clinical corpora for concept normalization, and categorized the distinct types of ambiguity they exhibited. We then compared observed string ambiguity in the datasets to potential ambiguity in the Unified Medical Language System (UMLS), to assess how representative available datasets are of ambiguity in clinical language.

Results: We observed twelve distinct types of ambiguity, distributed unequally across the available datasets. However, less than 15% of the strings were ambiguous within the datasets, while over 50% were ambiguous in the UMLS, indicating only partial coverage of clinical ambiguity.

Discussion: Existing datasets are not sufficient to cover the diversity of clinical concept ambiguity, limiting both training and evaluation of normalization methods for clinical text. Additionally, the UMLS offers important semantic information for building and evaluating normalization methods.

Conclusion: Our findings identify three opportunities for concept normalization research, including a need for ambiguity-specific clinical datasets and leveraging the rich semantics of the UMLS in new methods and evaluation measures for normalization.