The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings Article Swipe
Shorabuddin Syed
,
Adam Angel
,
Hafsa Bareen Syeda
,
Carole Jennings
,
Joseph VanScoy
,
Mahanazuddin Syed
,
Melody Greer
,
Sudeepa Bhattacharyya
,
Meredith Zozus
,
Benjamin Tharian
,
Fred Prior
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5220/0010903300003123
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5220/0010903300003123
Colonoscopy is a screening and diagnostic procedure for detection of colorectal carcinomas with specific quality metrics that monitor and improve adenoma detection rates. These quality metrics are stored in disparate documents i.e., colonoscopy, pathology, and radiology reports. The lack of integrated standardized documentation is impeding colorectal cancer research. Clinical concept extraction using Natural Language Processing (NLP) and Machine Learning (ML) techniques is an alternative to manual data abstraction. Contextual word embedding models such as BERT (Bidirectional Encoder Representations from Transformers) and FLAIR have enhanced performance of NLP tasks. Combining multiple clinically-trained embeddings can improve word representations and boost the performance of the clinical NLP systems. The objective of this study is to extract comprehensive clinical concepts from the consolidated colonoscopy documents using concatenated clinical embeddings. We built high-quality annotated corpora for three report types. BERT and FLAIR embeddings were trained on unlabeled colonoscopy related documents. We built a hybrid Artificial Neural Network (h-ANN) to concatenate and fine-tune BERT and FLAIR embeddings. To extract concepts of interest from three report types, 3 models were initialized from the h-ANN and fine-tuned using the annotated corpora. The models achieved best F1-scores of 91.76%, 92.25%, and 88.55% for colonoscopy, pathology, and radiology reports respectively.
Related Topics
Concepts
Computer science
Natural language processing
Artificial intelligence
Colonoscopy
Deep learning
Encoder
Fluid-attenuated inversion recovery
Documentation
Artificial neural network
Machine learning
Embedding
Information retrieval
Radiology
Colorectal cancer
Medicine
Cancer
Programming language
Operating system
Internal medicine
Magnetic resonance imaging
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5220/0010903300003123
- OA Status
- hybrid
- Cited By
- 11
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214481437
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4214481437Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5220/0010903300003123Digital Object Identifier
- Title
-
The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual EmbeddingsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
-
Shorabuddin Syed, Adam Angel, Hafsa Bareen Syeda, Carole Jennings, Joseph VanScoy, Mahanazuddin Syed, Melody Greer, Sudeepa Bhattacharyya, Meredith Zozus, Benjamin Tharian, Fred PriorList of authors in order
- Landing page
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https://doi.org/10.5220/0010903300003123Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5220/0010903300003123Direct OA link when available
- Concepts
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Computer science, Natural language processing, Artificial intelligence, Colonoscopy, Deep learning, Encoder, Fluid-attenuated inversion recovery, Documentation, Artificial neural network, Machine learning, Embedding, Information retrieval, Radiology, Colorectal cancer, Medicine, Cancer, Programming language, Operating system, Internal medicine, Magnetic resonance imagingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 5, 2023: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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