Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach
The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Viruses |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4915/14/12/2761 |
| _version_ | 1797454991251734528 |
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| author | Syed Raza Bashir Shaina Raza Veysel Kocaman Urooj Qamar |
| author_facet | Syed Raza Bashir Shaina Raza Veysel Kocaman Urooj Qamar |
| author_sort | Syed Raza Bashir |
| collection | DOAJ |
| description | The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1–5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics. |
| first_indexed | 2024-03-09T15:45:06Z |
| format | Article |
| id | doaj.art-c0bd9fd09c5e4a0ead0a2bbf0b729693 |
| institution | Directory Open Access Journal |
| issn | 1999-4915 |
| language | English |
| last_indexed | 2024-03-09T15:45:06Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj.art-c0bd9fd09c5e4a0ead0a2bbf0b7296932023-11-24T18:39:11ZengMDPI AGViruses1999-49152022-12-011412276110.3390/v14122761Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing ApproachSyed Raza Bashir0Shaina Raza1Veysel Kocaman2Urooj Qamar3Department of Computer Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaDalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, CanadaData Science, John Snow Labs Inc., Lewes, DE 19958, USAInstitute of Business & Information Technology, University of the Punjab, Lahore 54590, PakistanThe clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant of health (SDoH), are also important to study the infectious disease. In this paper, we propose a generalizable machine learning approach that improves on previous efforts by recognizing a large number of clinical risk factors and SDoH. The novelty of the proposed method lies in the subtle combination of a number of deep neural networks, including the BiLSTM-CNN-CRF method and a transformer-based embedding layer. Experimental results on a cohort of COVID-19 data prepared from PubMed articles show the superiority of the proposed approach. When compared to other methods, the proposed approach achieves a performance gain of about 1–5% in terms of macro- and micro-average F1 scores. Clinical practitioners and researchers can use this approach to obtain accurate information regarding clinical risks and SDoH factors, and use this pipeline as a tool to end the pandemic or to prepare for future pandemics.https://www.mdpi.com/1999-4915/14/12/2761COVID-19named entitiesclinicalnon-clinicalsocial determinants of healthpipeline |
| spellingShingle | Syed Raza Bashir Shaina Raza Veysel Kocaman Urooj Qamar Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach Viruses COVID-19 named entities clinical non-clinical social determinants of health pipeline |
| title | Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach |
| title_full | Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach |
| title_fullStr | Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach |
| title_full_unstemmed | Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach |
| title_short | Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach |
| title_sort | clinical application of detecting covid 19 risks a natural language processing approach |
| topic | COVID-19 named entities clinical non-clinical social determinants of health pipeline |
| url | https://www.mdpi.com/1999-4915/14/12/2761 |
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