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![Fig. 3 Component planes CP (map size [8, 3]) of 10 features and...](publication/342373482/figure/fig3/AS:905409946734592@1592877961028/Component-planes-CP-map-size-8-3-of-10-features-and-patient-outcom-created-by_Q320.jpg)
The pandemic spread of coronavirus leads to increased burden on healthcare services worldwide. Experience shows that required medical treatment can reach limits at local clinics and fast and secure clinical assessment of the disease severity becomes vital. In L. Yan et al. a model is presented for predicting the mortality of COVID-19 patients from...
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... this, the mutual correlation of feature elements are evaluated and a greedy algorithm searches MDS after determination a threshold for mutual feature correlation [7]. Figure 4 shows the resulting correlation graph. We see the key features cover very well the MDS when we restrict it to the features with good matches with patient outcome. ...
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Taking appropriate decisions in the academic processes at a university has a great impact in improving the quality of education and can have an important benefit for students, faculty members, and the entire academic community. In this paper, we propose a decision support solution providing accurate analysis, better decision support, and reporting...
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... The model's balanced accuracy for the mortality outcome was 80%, and its AUC value was 83%. Some ML algorithms have been developed to forecast the probability of severe complications and mortality [13,[40][41][42][43][44][45][46][47]. The list of previous works and their characteristics related to COVID-19 mortality can be found in Table 4. ...
... As a result, scalable solutions must be developed based on alternative data acquired using inexpensive and easily accessible tests. Various ML models have been built in this context to predict the risk for progression to severe COVID-19-related complications and mortality [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. The development of ML models is significantly relevant in a situation with limited resources compared with the sharp increase in the number of COVID-19 individuals. ...
The increase in coronavirus disease 2019 (COVID-19) infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed pressure on healthcare services worldwide. Therefore, it is crucial to identify critical factors for the assessment of the severity of COVID-19 infection and the optimization of an individual treatment strategy. In this regard, the present study leverages a dataset of blood samples from 485 COVID-19 individuals in the region of Wuhan, China to identify essential blood biomarkers that predict the mortality of COVID-19 individuals. For this purpose, a hybrid of filter, statistical, and heuristic-based feature selection approach was used to select the best subset of informative features. As a result, minimum redundancy maximum relevance (mRMR), a two-tailed unpaired t-test, and whale optimization algorithm (WOA) were eventually selected as the three most informative blood biomarkers: International normalized ratio (INR), platelet large cell ratio (P-LCR), and D-dimer. In addition, various machine learning (ML) algorithms (random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), naïve Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN)) were trained. The performance of the trained models was compared to determine the model that assist in predicting the mortality of COVID-19 individuals with higher accuracy, F1 score, and area under the curve (AUC) values. In this paper, the best performing RF-based model built using the three most informative blood parameters predicts the mortality of COVID-19 individuals with an accuracy of 0.96 ± 0.062, F1 score of 0.96 ± 0.099, and AUC value of 0.98 ± 0.024, respectively on the independent test data. Furthermore, the performance of our proposed RF-based model in terms of accuracy, F1 score, and AUC was significantly better than the known blood biomarkers-based ML models built using the Pre_Surv_COVID_19 data. Therefore, the present study provides a novel hybrid approach to screen the most informative blood biomarkers to develop an RF-based model, which accurately and reliably predicts in-hospital mortality of confirmed COVID-19 individuals, during surge periods. An application based on our proposed model was implemented and deployed at Heroku.
... Data obtained from approximately 117,000 patients worldwide are being extensively used for generating an AI methodology to predict the mortality chance of the patients affected with covid-19 (de Moraes Batista et al., 2020). In totality, five ML algorithms, including support vector machine, logistic regression, random forest, KNN, and gradient boosting algorithms, have been exploited to anticipate the mortality toll of the confirmed covid-19 affected patients of South Korea (Gemmar et al., 2020). A machine learning perspective is presented to effectively predict a patient's recovery (Pourhomayoun et al., 2020). ...
The ongoing COVID-19 pandemic has led to a major oppression of worldwide healthcare infrastructure. In current times, artificial intelligence (AI) and network medicine provide groundbreaking implementation of information science in defining diseases, therapeutics, medicines, and in associating targets with the minimum fallacy. In this big data era, artificial intelligence (AI) has immensely reduced the time and investment of novel targeted drug discovery. As there is continual unfolding of the results of the possible drug combinations, exploitation of artificial intelligence is of utmost necessity so as to hone combination therapy plan. Drug repositioning or repurposing is a methodology by means of which subsisting drugs are being manipulated to handle challenging and emerging diseases, including COVID-19. In this chapter, the authors present the regulations on how to use AI to expedite drug repurposing or repositioning, for which AI propositions are not only intimidating but are also inevitable.
... In recent publications, it has been shown that certain research studies employ artificial intelligence to aid in the analysis of computed tomography (CT) images 6 , while others use clinical data from patients to forecast infection progression 7 8 . These studies evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season, and their model was used to distinguish between COVID-19 and other typical viral pneumonia 9 , both of which have quite similar radiologic characteristics. 6 "The term "computed tomography", or CT, refers to a computerized x-ray imaging procedure in which a narrow beam of x-rays is aimed at a patient and quickly rotated around the body, producing signals that are processed by the machine's computer to generate cross-sectional images-or "slices"-of the body.", ...
... Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. 9 "Viral pneumonia is an infection of your lungs caused by a virus. The most common cause is the flu, but you can also get viral pneumonia from the common cold and other viruses.", ...
This study aims to find applicable combination of recent AI technologies and its uses during the COVID-19 pandemic. As AI has shown great improvements in the last few years, with many new feature-filled tools out on the market, it is almost certain that AI can help find ways to circumvent the dangers of COVID-19, in the form of noticing it, and preventing it. Authors cited in this work are AI professional, specializing in neural networks and their appliances in real world, in the form of visual processing and understanding, which will provide me with a solid hypothesis to base my master thesis on.
As this pandemic still has a solid grasp on the world, especially on less developed countries like Montenegro, I believe it makes a great testing ground for attributes like – percentage of people abiding pandemic specific laws (social distancing, mask wearing, etc.). Unfortunately, people of Montenegro are very divided on this issue, ranging from avid abiders, to those who completely ignore all safety procedures, even the most basic ones. As such, I believe this study will provide me with an insight into the right tools and tests for the residents of Montenegro, which vary a lot.
... A study of risk assessment of COVID-19 26 was proposed based on Fuzzy logic prediction system. It was successfully demonstrated to take into account the changes in risk over time. ...
Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.
... Different ML models have been proposed to predict risk of developing severe complications and mortality (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27). This is important since there are limited resources compared to the increasing number of COVID-19 patients. ...
The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.
It is significant to accurately predict the epidemic trend of COVID-19 due to its detrimental impact on the global health and economy. Although machine learning based approaches have been applied to predict epidemic trend, standard models have shown low accuracy for long-term prediction due to a high level of uncertainty and lack of essential training data. This paper proposes an improved machine learning framework employing Generative Adversarial Network (GAN) and Long Short-Term Memory (LSTM) for adversarial training to forecast the potential threat of COVID-19 in countries where COVID-19 is rapidly spreading. It also investigates the most updated COVID-19 epidemiological data before October 18, 2020 and model the epidemic trend as time series that can be fed into the proposed model for data augmentation and trend prediction of the epidemic. The proposed model is trained to predict daily numbers of cumulative confirmed cases of COVID-19 in Italy, USA, China, Germany, UK, and across the world. Paper further analyzes and suggests which populations are at risk of contracting COVID-19.
The coronavirus disease 2019 (COVID-19) is an acute respiratory disease that has been classified as a pandemic by World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public medical systems. Hence, it is crucial to identify the key factors of mortality that yield high accuracy and consistency to optimize patient treatment strategy. This study uses machine learning methods to identify a powerful combination of five features that help predict mortality with 96% accuracy: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP) and age. Various machine learning algorithms have been compared to achieve a consistent high accuracy across the days that span the disease. Robust testing with three cases confirm the strong predictive performance of the proposed model. The model predicts with an accuracy of 90% as early as 16 days before the outcome. This study would help accelerate the decision making process in healthcare systems for focused medical treatments early and accurately.
