Unsupervised Semantic Mapping for Healthcare Data Storage Schema Article Swipe
Fahad Ahmed Satti
,
Musarrat Hussain
,
Jamil Hussain
,
Syed Imran Ali
,
Taqdir Ali
,
Hafiz Syed Muhammad Bilal
,
TaeChoong Chung
,
Sungyoung Lee
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3100686
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3100686
Data, information, and knowledge processing systems, in the domain of healthcare, are currently plagued by heterogeneity at various levels. Current solutions have focused on developing a standard-based, manual intervention mechanism, which requires a large number of human resources and necessitates the realignment of existing systems. State-of-the-art methodologies in the field of natural language processing and machine learning can help to partially automate this process, reducing the resource requirements and providing a relatively good multi-class-based classification algorithm. We present a novel methodology for bridging the gap between various healthcare data management solutions by leveraging the strength of transformer-based machine learning models, to create mappings between the data elements. Additionally, the annotated data, collected against five medical schemas and labeled by four annotators is made available for helping future researchers. Our results indicate, that for biased, dependent multi-class text classification, transformer-based models provide better results than linguistic and other classical models. In particular, the Robustly Optimized BERT Pretraining Approach (RoBERTa) provides the best schema matching performance by achieving a Cohen’s kappa score of 0.47 and Matthews Correlation Coefficient (MCC) score of 0.48, with human-annotated data.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3100686
- https://ieeexplore.ieee.org/ielx7/6287639/9312710/09499053.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3188425338
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- OpenAlex ID
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https://openalex.org/W3188425338Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2021.3100686Digital Object Identifier
- Title
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Unsupervised Semantic Mapping for Healthcare Data Storage SchemaWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
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Fahad Ahmed Satti, Musarrat Hussain, Jamil Hussain, Syed Imran Ali, Taqdir Ali, Hafiz Syed Muhammad Bilal, TaeChoong Chung, Sungyoung LeeList of authors in order
- Landing page
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https://doi.org/10.1109/access.2021.3100686Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/9312710/09499053.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/9312710/09499053.pdfDirect OA link when available
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Computer science, Schema (genetic algorithms), Artificial intelligence, Transformer, Machine learning, Bridging (networking), Data mining, Quantum mechanics, Voltage, Physics, Computer networkTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 1, 2024: 2, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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