Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation Article Swipe
Asim Abbas
,
Muhammad Afzal
,
Jamil Hussain
,
Taqdir Ali
,
Hafiz Syed Muhammad Bilal
,
Sungyoung Lee
,
Seokhee Jeon
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph182010564
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijerph182010564
Extracting clinical concepts, such as problems, diagnosis, and treatment, from unstructured clinical narrative documents enables data-driven approaches such as machine and deep learning to support advanced applications such as clinical decision-support systems, the assessment of disease progression, and the intelligent analysis of treatment efficacy. Various tools such as cTAKES, Sophia, MetaMap, and other rules-based approaches and algorithms have been used for automatic concept extraction. Recently, machine- and deep-learning approaches have been used to extract, classify, and accurately annotate terms and phrases. However, the requirement of an annotated dataset, which is labor-intensive, impedes the success of data-driven approaches. A rule-based mechanism could support the process of annotation, but existing rule-based approaches fail to adequately capture contextual, syntactic, and semantic patterns. This study intends to introduce a comprehensive rule-based system that automatically extracts clinical concepts from unstructured narratives with higher accuracy and transparency. The proposed system is a pipelined approach, capable of recognizing clinical concepts of three types, problem, treatment, and test, in the dataset collected from a published repository as a part of the I2b2 challenge 2010. The system’s performance is compared with that of three existing systems: Quick UMLS, BIO-CRF, and the Rules (i2b2) model. Compared to the baseline systems, the average F1-score of 72.94% was found to be 13% better than Quick UMLS, 3% better than BIO CRF, and 30.1% better than the Rules (i2b2) model. Individually, the system performance was noticeably higher for problem-related concepts, with an F1-score of 80.45%, followed by treatment-related concepts and test-related concepts, with F1-scores of 76.06% and 55.3%, respectively. The proposed methodology significantly improves the performance of concept extraction from unstructured clinical narratives by exploiting the linguistic and lexical semantic features. The approach can ease the automatic annotation process of clinical data, which ultimately improves the performance of supervised data-driven applications trained with these data.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijerph182010564
- https://www.mdpi.com/1660-4601/18/20/10564/pdf?version=1633772489
- OA Status
- gold
- Cited By
- 11
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3206589498
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3206589498Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ijerph182010564Digital Object Identifier
- Title
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Clinical Concept Extraction with Lexical Semantics to Support Automatic AnnotationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-10-09Full publication date if available
- Authors
-
Asim Abbas, Muhammad Afzal, Jamil Hussain, Taqdir Ali, Hafiz Syed Muhammad Bilal, Sungyoung Lee, Seokhee JeonList of authors in order
- Landing page
-
https://doi.org/10.3390/ijerph182010564Publisher landing page
- PDF URL
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https://www.mdpi.com/1660-4601/18/20/10564/pdf?version=1633772489Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/1660-4601/18/20/10564/pdf?version=1633772489Direct OA link when available
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Annotation, Computer science, Semantics (computer science), Lexical semantics, Natural language processing, Extraction (chemistry), Artificial intelligence, Information retrieval, Lexical item, Programming language, Chemistry, ChromatographyTop concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
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2025: 1, 2024: 3, 2023: 3, 2022: 4Per-year citation counts (last 5 years)
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39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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