COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis

Ghenea, Alice Elena; Udriştoiu, Ştefan; Neaga, Manuela; Zlatian, Ovidiu; Vasile, Corina Maria; Popescu, Mihaela; Țieranu, Eugen; Salan, Alex-Ioan; Turcu, Adina Andreea; Nicolosu, Dragos; Calina, Daniela; Cioboată, Ramona

doi:10.3390/life11111281

Cited by 12 publications

(15 citation statements)

References 42 publications

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“…Continued research on interpretability in graph machine models remains an important area of future research. Moreover, in addition to the diseases mentioned above, other diseases such as COVID-19 [ 80 ] and thyroid diseases [ 81 ] are currently of concern. It is also worth investigating how to use graph machine-learning techniques to predict these diseases.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends

Uddin

2023

Healthcare

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Graph machine-learning (ML) methods have recently attracted great attention and have made significant progress in graph applications. To date, most graph ML approaches have been evaluated on social networks, but they have not been comprehensively reviewed in the health informatics domain. Herein, a review of graph ML methods and their applications in the disease prediction domain based on electronic health data is presented in this study from two levels: node classification and link prediction. Commonly used graph ML approaches for these two levels are shallow embedding and graph neural networks (GNN). This study performs comprehensive research to identify articles that applied or proposed graph ML models on disease prediction using electronic health data. We considered journals and conferences from four digital library databases (i.e., PubMed, Scopus, ACM digital library, and IEEEXplore). Based on the identified articles, we review the present status of and trends in graph ML approaches for disease prediction using electronic health data. Even though GNN-based models have achieved outstanding results compared with the traditional ML methods in a wide range of disease prediction tasks, they still confront interpretability and dynamic graph challenges. Though the disease prediction field using ML techniques is still emerging, GNN-based models have the potential to be an excellent approach for disease prediction, which can be used in medical diagnosis, treatment, and the prognosis of diseases.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends

Uddin

2023

Healthcare

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“…In particular, 5 of the included studies did not report details of patient selection [29,31,32,35,43], and 4 provided unclear information on patient selection [30,40,48,50], resulting in a high and unclear bias in patient selection. Moreover, the threshold was not prespecified in one study [39], leading to a high risk of bias in the index test, and 8 studies provided unclear information on how to perform the index test [30,[34][35][36][37][38]42,46], leading to an unclear risk of bias. Furthermore, one study interpreted the results of reference standards when the results of the index test were known [41], leading to a high risk of bias in the reference standard, and another did not explain this [28], which was considered to be an unclear risk of bias.…”

Section: Quadas-2mentioning

confidence: 99%

“…However, only 4 of the 23 articles used this approach to select predictor variables [41,44,45,47]. Of the remaining articles, 10 adopted univariate variables [29][30][31][32]37,39,40,42,43,46], and 9 used variables with significant levels in clinical analyses [28,[33][34][35][36]38,[48][49][50]. However, univariate variables or variables with significant levels in clinical analyses may not be suitable as candidate predictors [60].…”

Section: Predictor Variablesmentioning

confidence: 99%

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Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis

Wang¹,

Liu²,

Tang³

et al. 2023

J Med Internet Res

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Background Deep learning (DL) prediction models hold great promise in the triage of COVID-19. Objective We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19. Methods We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability. Results A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I2=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I2=0.00%) and 0.80 (95% CI 0.76-0.83), respectively. Conclusions DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking. Trial Registration PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252

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“…14,20 Artificial intelligence (AI), machine learning (ML), and data mining (DM) methods have played an important role in coronavirus research. 16,21,22 According to studies, these methods are effective and well-known tools for developing predictive and data analysis models and extracting useful information from the available data set 23,24 applied the Support Vector Machine (SVM) technique to demographic, clinical, and laboratory data of patients with COVID-19 to predict their ICU admission, mortality rate, and length of hospital stay. Also, 25 used laboratory data sets to predict the intensity of COVID-19 using data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

Discovering associations between radiological features and COVID‐19 patients' deterioration

Ahmadinejad

Ayyoubzadeh

Zeinalkhani

et al. 2023

Health Science Reports

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Background and AimsData mining methods are effective and well‐known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID‐19 by applying the rule mining method using characteristics of medical images.MethodsThis retrospective study has analyzed the radiological data from 104 COVID‐19 hospitalized patients diagnosed with COVID‐19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes.ResultsTen rules were extracted with only X‐ray‐related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan‐related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13.ConclusionThis study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID‐19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes.

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COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis

Cited by 12 publications

References 42 publications

Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends

Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends

Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis

Discovering associations between radiological features and COVID‐19 patients' deterioration

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