Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data

Dinar, Ahmed M.; Raheem, Enas A.; Abdulkareem, Karrar Hameed; Mohammed, Mazin Abed; Oleiwie, Marwan Ghazi; Zayr, Fawzi Hasan; Al-Boridi, Omar; Al-Mhiqani, Mohammed Nasser; Al-Andoli, Mohammed Nasser

doi:10.1155/2022/7675925

Cited by 8 publications

(14 citation statements)

References 26 publications

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“…Dataset was extracted from studies [24,25] The data used in the development of the models was collected between April 8th and March 12th, 2020. Azizia Primary Healthcare Sector-Wasit Governorate-Iraq confirmed the diagnosis of Covid-19 in 78 patients under the care of medical experts.…”

Section: Datasetmentioning

confidence: 99%

“…In addition, the dataset utilized was collected from medical testing facilities and the patient's vital functions, which can result in high technical accuracy but can also confound seasonal flu and other viral flu [22,23]. Further, in study [24], the authors make extensive efforts to predict the severity of covid-19 patients using a combination of different observations, but the classification performance is unsatisfactory. This work's key contributions may be summed up as follows: • Suggest a Lasso-Logistic Regression-based multi-class case severity prediction method for earlystage COVID-19 infections.…”

Section: Introductionmentioning

confidence: 99%

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Severity Classification for COVID-19 Infections based on Lasso-Logistic Regression Model

Arif¹,

Cengiz

2023

ijmscs

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The tremendous growth of the Covid19 epidemic in recent months is devastatingly affecting human civilization. Many different biomarkers are being studied to monitor the patient's health. This might mask the symptoms of various diseases, making it more challenging for a doctor to make a correct diagnosis or prognosis. Therefore, this study aimed to create several classes of prediction methods that can handle situations of varying severity (severe, moderate, and mild). Using machine learning, a Lasso-logistic regression model is developed. To create the Covid-19 clinical dataset, researchers enlisted the help of 78 patients from the Azizia main hospital sector, the Wasit Health Directorate, and the Ministry of Health. The results show that the proposed method is generally accurate to 85.9%. Deaths have been reduced thanks to the established prediction method that enables early detection of patients across three severity levels.

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Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Severity Classification for COVID-19 Infections based on Lasso-Logistic Regression Model

Arif¹,

Cengiz

2023

ijmscs

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“…It consists of a list of COVID-19 patients, who are considered to be alternatives, and medical sources, which are considered to be the evaluation criteria. The real dataset that was used in our proposed study was extracted from References [ 8 , 48 ]. The total number of people infected with the COVID-19 virus was 78; they were diagnosed under the supervision of specialized doctors and were distributed into Al-Aziziyah Hospital in Wasit Governorate of Iraq.…”

Section: The Proposed Multidimensional Framework For Covid-19 Patient...mentioning

confidence: 99%

“…All of these criteria are crucial in monitoring a COVID-19 patient’s status, but the large number of these indicators poses a challenge for doctors and emergency services to decide or predict the patients’ severity [ 8 ]. Furthermore, the old and those suffering from chronic diseases such as diabetes, cancer identification, chronic respiratory disease, cardiovascular disease, and chronic respiratory disease are at a higher risk of contracting a serious infection [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches

et al. 2022

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Effective prioritization plays critical roles in precision medicine. Healthcare decisions are complex, involving trade-offs among numerous frequently contradictory priorities. Considering the numerous difficulties associated with COVID-19, approaches that could triage COVID-19 patients may help in prioritizing treatment and provide precise medicine for those who are at risk of serious disease. Prioritizing a patient with COVID-19 depends on a variety of examination criteria, but due to the large number of these biomarkers, it may be hard for medical practitioners and emergency systems to decide which cases should be given priority for treatment. The aim of this paper is to propose a Multidimensional Examination Framework (MEF) for the prioritization of COVID-19 severe patients on the basis of combined multi-criteria decision-making (MCDM) methods. In contrast to the existing literature, the MEF has not considered only a single dimension of the examination factors; instead, the proposed framework included different multidimensional examination criteria such as demographic, laboratory findings, vital signs, symptoms, and chronic conditions. A real dataset that consists of data from 78 patients with different examination criteria was used as a base in the construction of Multidimensional Evaluation Matrix (MEM). The proposed framework employs the CRITIC (CRiteria Importance Through Intercriteria Correlation) method to identify objective weights and importance for multidimensional examination criteria. Furthermore, the VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is utilized to prioritize COVID-19 severe patients. The results based on the CRITIC method showed that the most important examination criterion for prioritization is COVID-19 patients with heart disease, followed by cough and nasal congestion symptoms. Moreover, the VIKOR method showed that Patients 8, 3, 9, 59, and 1 are the most urgent cases that required the highest priority among the other 78 patients. Finally, the proposed framework can be used by medical organizations to prioritize the most critical COVID-19 patient that has multidimensional examination criteria and to promptly give appropriate care for more precise medicine.

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“…Basic performance indicators including the R , RMSE , MAPE , MAE , NS , and a20-index were taken into account while evaluating the effectiveness of the CF and ANN models in order to determine the models’ reliability [ 64 , 65 , 66 , 67 , 68 ]. It is clear that COVID-19 has affected many areas of our life, including our environment, activities, and other factors, and may require intelligent solutions using AI techniques, medical images, and clinical data to control the pandemic [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ]. Formulas (11) to (16) below give the equations for the performance indices listed above.…”

Section: R-event Prognosismentioning

confidence: 99%

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

Kapoor

Kumar

et al. 2022

IJERPH

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The emerging novel variants and re-merging old variants of SARS-CoV-2 make it critical to study the transmission probability in mixed-mode ventilated office environments. Artificial neural network (ANN) and curve fitting (CF) models were created to forecast the R-Event. The R-Event is defined as the anticipated number of new infections that develop in particular events occurring over the course of time in any defined space. In the spring and summer of 2022, real-time data for an office environment were collected in India in a mixed-mode ventilated office space in a composite climate. The performances of the proposed CF and ANN models were compared with respect to traditional statistical indicators, such as the correlation coefficient, RMSE, MAE, MAPE, NS index, and a20-index, in order to determine the merit of the two approaches. Thirteen input features, namely the indoor temperature (TIn), indoor relative humidity (RHIn), area of opening (AO), number of occupants (O), area per person (AP), volume per person (VP), CO2 concentration (CO2), air quality index (AQI), outer wind speed (WS), outdoor temperature (TOut), outdoor humidity (RHOut), fan air speed (FS), and air conditioning (AC), were selected to forecast the R-Event as the target. The main objective was to determine the relationship between the CO2 level and R-Event, ultimately producing a model for forecasting infections in office building environments. The correlation coefficients for the CF and ANN models in this case study were 0.7439 and 0.9999, respectively. This demonstrates that the ANN model is more accurate in R-Event prediction than the curve fitting model. The results show that the proposed ANN model is reliable and significantly accurate in forecasting the R-Event values for mixed-mode ventilated offices.

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Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data

Cited by 8 publications

References 26 publications

Severity Classification for COVID-19 Infections based on Lasso-Logistic Regression Model

Severity Classification for COVID-19 Infections based on Lasso-Logistic Regression Model

MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches

Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN

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