Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach

Bashir, Syed Raza; Raza, Shaina; Kocaman, Veysel; Qamar, Urooj

doi:10.3390/v14122761

Cited by 2 publications

(1 citation statement)

References 45 publications

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“…Preprocessing is converting data into a format that can be used easily by ML algorithms to process efectively. Some of the preprocessing steps [26] are (i) Tokenization: this is the process of breaking the input text into individual words (or tokens). Tis is usually done by splitting the text based on spaces or punctuation.…”

Section: Preprocessingmentioning

confidence: 99%

An Intelligent COVID-19-Related Arabic Text Detection Framework Based on Transfer Learning Using Context Representation

Muaad,

Raza,

Heyat

et al. 2024

International Journal of Intelligent Systems

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The misleading information during the coronavirus disease 2019 (COVID-19) pandemic’s peak time is very sensitive and harmful in our community. Analyzing and detecting COVID-19 information on social media are a crucial task. Early detection of COVID-19 information is very helpful and minimizes the risk of psychological security which leads to inconvenience in daily life. In this paper, a deep ensemble transfer learning framework with an understanding of the context of Arabic text COVID-19 information is proposed. This framework is inspired to spontaneously analyze and recognize the text about COVID-19. The ArCOVID-19Vac dataset has been used to train and test our proposed model. A comprehensive experimental study for each scenario is performed. For the binary classification scenario, the proposed framework records better evaluation results with 83.0%, 84.0%, 83.0%, and 84.0% in terms of accuracy, precision, recall, and F1-score, respectively. For the second scenario (three classes), the overall performance is recorded with an accuracy of 82.0%, precision of 80.0%, recall of 82.0%, and F1-score of 80.0%, respectively. In the last scenario with ten classes, the best evaluation performance results are recorded with an accuracy of 67.0%, a precision of 58.0%, a recall of 67.0%, and F1-score of 59.0%, respectively. In addition, we have applied an ensemble transfer learning model for this scenario to get 64.0%, 66.0%, 66.0%, and 65.0% in terms of accuracy, precision, recall, and F1-score, respectively. The results show that the proposed model through transfer learning provides better results for Arabic text than all state-of-the-art methods.

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Section: Preprocessingmentioning

confidence: 99%

An Intelligent COVID-19-Related Arabic Text Detection Framework Based on Transfer Learning Using Context Representation

Muaad,

Raza,

Heyat

et al. 2024

International Journal of Intelligent Systems

Self Cite

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Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Li,

Mowery,

et al. 2024

Preprint

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Background Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. Methods We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes. Discussion Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfilling the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

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Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach

Cited by 2 publications

References 45 publications

An Intelligent COVID-19-Related Arabic Text Detection Framework Based on Transfer Learning Using Context Representation

An Intelligent COVID-19-Related Arabic Text Detection Framework Based on Transfer Learning Using Context Representation

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

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