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

Abdulkareem, Karrar Hameed; Al-Mhiqani, Mohammed Nasser; Dinar, Ahmed M.; Mohammed, Mazin Abed; Al-Imari, Mustafa Jawad; Al-Waisy, Alaa S.; Alghawli, Abed Saif; Al-qaness, Mohammed A. A.

doi:10.3390/bioengineering9090457

Cited by 15 publications

(5 citation statements)

References 57 publications

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“…While reference [20] identifies three important indicators (lymphocyte, CRP, and SpO2) that have been proven pathologically and technically. Reference [21] proposes a Multidimensional Examination Framework (MEF) for the prioritization of COVID-19 severe patients based on combined multi-criteria decision-making (MCDM) methods.…”

Section: Background and Motivationmentioning

confidence: 99%

Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

Mukhlif,

Ithnin,

Alroobaea

et al. 2023

CMC

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The World Health Organization (WHO) refers to the 2019 new coronavirus epidemic as COVID-19, and it has caused an unprecedented global crisis for several nations. Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks, which were previously only experienced by Chinese residents. Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems. Every time the pandemic surprises them by providing new values for various parameters, all the connected research groups strive to understand the behavior of the pandemic to determine when it will stop. The prediction models in this research were created using deep neural networks and Decision Trees (DT). DT employs the support vector machine method, which predicts the transition from an initial dataset to actual figures using a function trained on a model. Extended short-term memory networks (LSTMs) are a special sort of recurrent neural network (RNN) that can pick up on long-term dependencies. As an added bonus, it is helpful when the neural network can both recall current events and recall past events, resulting in an accurate prediction for COVID-19. We provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring framework. We developed a data analysis methodology, including data preparation and dataset splitting. We examine two popular algorithms, LSTM and Decision tree on the official datasets. Moreover, we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the pandemic. Key issues and challenges are discussed for future improvement. It is expected that the results these methods provide for the Health Scenario would be reliable and credible.

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Section: Background and Motivationmentioning

confidence: 99%

Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

Mukhlif,

Ithnin,

Alroobaea

et al. 2023

CMC

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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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“…Early detection of positive cases is an impressive method to control the outbreak rate ( Abdulkareem et al, 2022 ). The conventional way to detect COVID-19 is a real-time reverse transcription-polymerase chain reaction (RT-PCR).…”

Section: Introductionmentioning

confidence: 99%

A novel framework based on deep learning for COVID-19 diagnosis from X-ray images

Javadi-Moghaddam¹

2023

PeerJ Computer Science

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Background The coronavirus infection has endangered human health because of the high speed of the outbreak. A rapid and accurate diagnosis of the infection is essential to avoid further spread. Due to the cost of diagnostic kits and the availability of radiology equipment in most parts of the world, the COVID-19 detection method using X-ray images is still used in underprivileged countries. However, they are challenging due to being prone to human error, time-consuming, and demanding. The success of deep learning (DL) in automatic COVID-19 diagnosis systems has necessitated a detection system using these techniques. The most critical challenge in using deep learning techniques in diagnosing COVID-19 is accuracy because it plays an essential role in controlling the spread of the disease. Methods This article presents a new framework for detecting COVID-19 using X-ray images. The model uses a modified version of DenseNet-121 for the network layer, an image data loader to separate images in batches, a loss function to reduce the prediction error, and a weighted random sampler to balance the training phase. Finally, an optimizer changes the attributes of the neural networks. Results Extensive experiments using different types of pneumonia expresses satisfactory diagnosis performance with an accuracy of 99.81%. Conclusion This work aims to design a new deep neural network for highly accurate online recognition of medical images. The evaluation results show that the proposed framework can be considered an auxiliary device to help radiologists accurately confirm initial screening.

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

Cited by 15 publications

References 57 publications

Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

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

A novel framework based on deep learning for COVID-19 diagnosis from X-ray images

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