COVID-19 studies involving machine learning methods: A bibliometric study

Baygül Eden, Arzu; Bakir Kayi, Alev; Erdem, Mustafa Genco; Demirci, Mehmet

doi:10.1097/md.0000000000035564

Cited by 2 publications

(1 citation statement)

References 34 publications

(59 reference statements)

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“…The use of AI and ML in the context of the pandemic is centered on pattern detections that can be obtained from medical images to laboratory parameters. However, AI is not limited to this, since it can be equally used in therapy, prognosis and also extremely useful in public health management (7)(8)(9). These tools are invaluable for understanding, predicting, and responding to the spread of COVID-19, demonstrating their ability to provide data-based information and facilitate evidence-based decision-making.…”

Section: Introductionmentioning

confidence: 99%

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data

Lourenço,

Amaral,

Paim

et al. 2024

Front. Public Health

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The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack of tools to combine these parameters to predict the clinical outcome of a patient with COVID-19. Thus, this study aimed at employing hematological and biochemical parameters of patients diagnosed with COVID-19 in order to build machine learning algorithms for predicting COVID mortality or survival. Patients included in the study had a diagnosis of SARS-CoV-2 infection confirmed by RT-PCR and biochemical and hematological measurements were performed in three different time points upon hospital admission. Among the parameters evaluated, the ones that stand out the most are the important features of the T1 time point (urea, lymphocytes, glucose, basophils and age), which could be possible biomarkers for the severity of COVID-19 patients. This study shows that urea is the parameter that best classifies patient severity and rises over time, making it a crucial analyte to be used in machine learning algorithms to predict patient outcome. In this study optimal and medically interpretable machine learning algorithms for outcome prediction are presented for each time point. It was found that urea is the most paramount variable for outcome prediction over all three time points. However, the order of importance of other variables changes for each time point, demonstrating the importance of a dynamic approach for an effective patient’s outcome prediction. All in all, the use of machine learning algorithms can be a defining tool for laboratory monitoring and clinical outcome prediction, which may bring benefits to public health in future pandemics with newly emerging and reemerging SARS-CoV-2 variants of concern.

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Section: Introductionmentioning

confidence: 99%

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data

Lourenço,

Amaral,

Paim

et al. 2024

Front. Public Health

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Machine Learning Applications in Prediction Models for COVID-19: A Bibliometric Analysis

Lv,

Liu,

Yin

et al. 2024

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The COVID-19 pandemic has had a profound impact on global health, inspiring the widespread use of machine learning in combating the disease, particularly in prediction models. This study aimed to assess academic publications utilizing machine learning prediction models to combat COVID-19. We analyzed 2422 original articles published between 2020 and 2023 with bibliometric tools such as Histcite Pro 2.1, Bibliometrix, CiteSpace, and VOSviewer. The United States, China, and India emerged as the most prolific countries, with Stanford University producing the most publications and Huazhong University of Science and Technology receiving the most citations. The National Natural Science Foundation of China and the National Institutes of Health have made significant contributions to this field. Scientific Reports is the most frequent journal for publishing these articles. Current research focuses on deep learning, federated learning, image classification, air pollution, mental health, sentiment analysis, and drug repurposing. In conclusion, this study provides detailed insights into the key authors, countries, institutions, funding agencies, and journals in the field, as well as the most frequently used keywords.

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COVID-19 studies involving machine learning methods: A bibliometric study

Cited by 2 publications

References 34 publications

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data

Machine Learning Applications in Prediction Models for COVID-19: A Bibliometric Analysis

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