Machine learning models for predicting diagnosis or prognosis of COVID-19: A systematic review

Dabbagh, Rufaidah; Jamal, Amr; Temsah, Mohamad-Hani; Masud, Jakir Hossain Bhuiyan; Titi, Maher A.; Amer, Yasser Sami; Alkubeyyer, Metab; Alhazmi, Taha; Baothman, Fatimah; Hneiny, Layal

doi:10.1016/j.cmpb.2021.105993

Cited by 5 publications

(7 citation statements)

References 6 publications

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“…The most common ML methods in the reviewed studies were CNN 34–85 . In line with this result, other review studies also indicated CNN to be the most common method to develop a system for COVID‐19 diagnosis 23,25,27,28,30,100 . A CNN is a type of DL algorithm used for processing medical images, particularly for identifying specific features in chest radiographs of COVID‐19 patients 30 .…”

Section: Discussionmentioning

confidence: 74%

“…In line with this result, other review studies also indicated CNN to be the most common method to develop a system for COVID-19 diagnosis. 23,25,27,28,30,100 A CNN is a type of DL algorithm used for processing medical images, particularly for identifying specific features in chest radiographs of COVID-19 patients. 30 CNN is more valuable than other methods for developing CDSS due to its excellent performance accuracy and much lower preprocessing.…”

Section: Nonknowledge-based Cdssmentioning

confidence: 99%

“…20,22,23 One of the most widely used subsets of DL is the Convolutional Neural Network (CNN), which allows the system to learn data representation. 20,22,23 Previous review studies have examined the impact of AI or ML models in screening, diagnostics, and prediction of COVID-19, [24][25][26][27][28] ML models for diagnosing infectious diseases, 23 CDSS based on AI for early recognition of respiratory infections, 29 and CNN for the diagnosis and prognosis of COVID-19. 30…”

Section: Introductionmentioning

confidence: 99%

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Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Ameri,

Salmanizadeh

et al. 2024

Health Science Reports

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Background and AimsDue to the COVID‐19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID‐19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID‐19.MethodsWe searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID‐19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher.ResultsA search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge‐based CDSS (n = 52) and knowledge‐based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS.ConclusionCDSS for COVID‐19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision‐making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge‐based and knowledge‐based CDSS in COVID‐19 diagnosis.

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Section: Discussionmentioning

confidence: 74%

Section: Nonknowledge-based Cdssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Ameri,

Salmanizadeh

et al. 2024

Health Science Reports

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“…These studies suggest that ML tecniques are able to unlock the predictive power of non-linear relationships between blood biomakers [8] . Therefore, in addition to the most common assessment methods used to monitor the progress of pulmonary disease, such as X-rays and CT-scan images, blood tests could also be used as indicators of the COVID-19 severity [9] , [10] . One of the first studies to address the problem was the retrospective proposed by Yan et al (2020) [11] .…”

Section: Introductionmentioning

confidence: 99%

Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil

Araújo

Veloso

Borges

et al. 2022

International Journal of Medical Informatics

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“…With the global spread of the novel coronavirus disease-2019 (COVID- 19), the use of technology to support patient care has seen a rapid rise. Technological tools were used to support patient care during the COVID-19 pandemic crisis through early detection of suspected individuals with the virus using artificial intelligence [1,2], generating big data analytics, reporting real-time data [3], and providing virtual care to patients [4]. The use of electronic health records (EHRs) to support patient care during COVID-19 has also been a focus of many healthcare organizations [5].…”

Section: Introductionmentioning

confidence: 99%

Stress and Burnout Related to Electronic Health Record Use among Healthcare Providers during the COVID-19 Pandemic in Saudi Arabia: A Preliminary National Randomized Survey

et al. 2021

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Healthcare providers’ burnout may potentially have a negative impact on patient care. The use of the electronic health record (EHR) increases the burden for healthcare providers (HCPs), particularly during the coronavirus disease-2019 (COVID-19) pandemic. This study assessed the stress and burnout related to the use of EHRs and health information technology (HIT) tools among HCPs during COVID-19 in Saudi Arabia. We used a self-developed survey tool. It consisted of five sections; demographics and professional data, experience using EHR, effects of EHR use, use of EHR and technology tools during COVID-19, and health and wellbeing. The survey link was emailed to a random sample of HCPs registered with a national scientific regulatory body. Univariate, bivariate, and multivariate analyses were performed to measure the association between burnout and study variables. A total of 182 participants completed the survey. 50.5% of participants reported a presence of HIT-related stress, and 40.1% reported a presence of burnout. The variables independently associated with burnout were providing tertiary level of care, working with COVID-19 suspected cases, dissatisfaction with EHRs, and agreement with the statement that using EHRs added frustration to the workday. Further research that explores possible solutions is warranted to minimize burnout among HCPs, especially during infectious outbreaks.

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Machine learning models for predicting diagnosis or prognosis of COVID-19: A systematic review

Cited by 5 publications

References 6 publications

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil

Stress and Burnout Related to Electronic Health Record Use among Healthcare Providers during the COVID-19 Pandemic in Saudi Arabia: A Preliminary National Randomized Survey

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