Determination of the Most Important Diagnostic Criteria for COVID-19: A Step forward to Design an Intelligent Clinical Decision Support System

Shanbehzadeh, Mostafa; Nopour, Raoof; Kazemi-Arpanahi, Hadi

doi:10.30699/jambs.29.134.176

Cited by 7 publications

(12 citation statements)

References 30 publications

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“…Performing a through‐full study probe search and estimating the different coinfections species pooled prevalence were our study's strengths. Because of increasing rate of pathogens coinfection prevalence in COVID‐19 patients, we suggest that a world registry will be developed in order to screen the pattern of coinfections 95,96 …”

Section: Discussionmentioning

confidence: 99%

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Worldwide prevalence of microbial agents’ coinfection among COVID‐19 patients: A comprehensive updated systematic review and meta‐analysis

Pakzad

Malekifar

Shateri

et al. 2021

Clinical Laboratory Analysis

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Background To provide information about pathogens’ coinfection prevalence with SARS‐CoV‐2 could be a real help to save patients’ lives. This study aims to evaluate the pathogens’ coinfection prevalence among COVID‐19 patients. Method In order to find all of the relevant articles, we used systematic search approach. Research‐based databases including PubMed, Web of Science, Embase, and Scopus, without language restrictions, were searched to identify the relevant bacterial, fungal, and viral coinfections among COVID‐19 cases from December 1, 2019, to August 23, 2021. In order to dig deeper, other scientific repositories such as Medrxiv were probed. Results A total of 13,023 studies were found through systematic search. After thorough analysis, only 64 studies with 61,547 patients were included in the study. The most common causative agents of coinfection among COVID‐19 patients were bacteria (pooled prevalence: 20.97%; 95% CI: 15.95–26.46; I2: 99.9%) and less frequent were virus coinfections (pooled prevalence: 12.58%; 95% CI: 7.31–18.96; I2: 98.7%). The pooled prevalence of fungal coinfections was also 12.60% (95% CI: 7.84–17.36; I2: 98.3%). Meta‐regression analysis showed that the age sample size and WHO geographic region did not influenced heterogeneity. Conclusion We identified a high prevalence of pathogenic microorganism coinfection among COVID‐19 patients. Because of this rate of coinfection empirical use of antibacterial, antifungal, and antiviral treatment are advisable specifically at the early stage of COVID‐19 infection. We also suggest running simultaneously diagnostic tests to identify other microbiological agents’ coinfection with SARS‐CoV‐2.

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Section: Discussionmentioning

confidence: 99%

“…Because of increasing rate of pathogens coinfection prevalence in COVID‐19 patients, we suggest that a world registry will be developed in order to screen the pattern of coinfections. 95 , 96 …”

Section: Discussionmentioning

confidence: 99%

Worldwide prevalence of microbial agents’ coinfection among COVID‐19 patients: A comprehensive updated systematic review and meta‐analysis

Pakzad

Malekifar

Shateri

et al. 2021

Clinical Laboratory Analysis

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“…These studies also recommended the use of CDSS in future research. Finally, 68 articles met all the inclusion criteria 5,17,34–99 . The flowchart of the selection process is shown in Figure 1.…”

Section: Resultsmentioning

confidence: 99%

“…Types of CDSS to assist in diagnosing COVID‐19 are shown in Figure 4. Most of the studies used ICDSS based on ML (nonknowledge‐based CDSS) ( n = 52 [76.5%]) 34–85 . In these studies, the most common methods for designing CDSS were CNN ( n = 33), 38,40–42,45–47,49–52,54,56–69,71,72,78,82–85 SVM ( n = 8), 35,36,39,43,44,54,56,57 RF ( n = 7), 34,35,37,39,42,44,55 and KNN ( n = 7) 36,37,39,42,43,55,56 (Table 1 and Appendix ).…”

Section: Resultsmentioning

confidence: 99%

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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“…In this situation, enhancing the capability of the healthcare system against the pandemic requires attention to technological and intelligent-based solutions such as Clinical Decision Support Systems (CDSSs) [ 16 , 17 ]. CDSSs attracted increasing interest because of the growing availability of a large amount of patient-level data [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach

Afrash

Kazemi-Arpanahi²,

Shanbehzadeh

et al. 2022

Informatics in Medicine Unlocked

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Determination of the Most Important Diagnostic Criteria for COVID-19: A Step forward to Design an Intelligent Clinical Decision Support System

Cited by 7 publications

References 30 publications

Worldwide prevalence of microbial agents’ coinfection among COVID‐19 patients: A comprehensive updated systematic review and meta‐analysis

Worldwide prevalence of microbial agents’ coinfection among COVID‐19 patients: A comprehensive updated systematic review and meta‐analysis

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

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach

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