Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach.
Viruses
; 14(12)2022 12 11.
Article
in English
| MEDLINE | ID: covidwho-2155318
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Natural Language Processing
/
COVID-19
Type of study:
Cohort study
/
Diagnostic study
/
Observational study
/
Prognostic study
/
Reviews
Limits:
Humans
Language:
English
Year:
2022
Document Type:
Article
Affiliation country:
V14122761
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