Deep Learning applications for COVID-19

Shorten, Connor; Khoshgoftaar, Taghi M.; Furht, Borko

doi:10.1186/s40537-020-00392-9

Cited by 270 publications

(179 citation statements)

References 99 publications

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“…This classifier is often explored in medical applications because it allows a good visualisation of the decision [22]; but, according to studies in [39,62,63], the Neural Network and Deep Leaning methods are effective and have the best accuracy for many applications. The Deep Leaningbased method has been used for COVID-19 detection based on X-ray image analysis [64], natural language processing [65], and epidemiology forecasting [66]. The Deep Learning and Neural Network methods can be effective for a reliability analysis.…”

Section: Discussionmentioning

confidence: 99%

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

2021

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Health and safety is a problem that is intensively discussed nowadays. The failures in healthcare are called medical errors: if the patient’s condition worsens or he/she contracts an illness, then the actions that led to this are interpreted as medical errors. Medical errors can be the result of new procedures, extremes of age, complex or urgent care, improper documentation, illegible hand-writing, or patient actions. One of the ways to reduce medical error is an evaluation of its possibility, and then using the result of this evaluation to improve the medical organization units and processes in patient diagnosis, treatment, and care. This evaluation is possible based on methods of reliability engineering. The reliability engineering methods allow evaluating of different systems’ reliability and the influence of external and internal factors on system reliability. These methods’ application needs the system to be investigated or objective interpretation in terms of reliability engineering. Therefore, such a system in healthcare, for the diagnosis of disease, a patient’s treatment, the influence of different factors on a patient’s condition, and others, should be presented according to the rules and demands of reliability engineering. The first step is development of the mathematical representation of the investigated system or object according to the demands of the reliability analysis. One of the often-used mathematical representations in the reliability analysis of a system is the structure function. However, this mathematical representation needs completely specified initial data. The initial data from the healthcare domain for medical error analysis is uncertain and incompletely specified. Therefore, the development of this mathematical representation needs special methods. In this paper, a new method for the mathematical representation of system development based on uncertain and incompletely specified data is proposed. The system evaluation based on the structure function allows computing of many reliability indices and measures used in reliability engineering. The approbation of this method is considered based on an example of COVID-19 patients.

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Section: Discussionmentioning

confidence: 99%

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

2021

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“…(Al-Rakhami and Al-Amri 2020) 85.5% of accuracy was predicted using Navis Bayes algorithm. (Shorten, Khoshgoftaar, and Furht 2021) implemented deep learning models with an accuracy of 90.3%.…”

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Identifying Accuracy of Online Misinformation of Covid-19 Using SVM Algorithm with Decision Tree Classification

Pravallika¹,

Rekha²

2021

alinteri

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Aim: To improve the accuracy percentage of predicting misinformation about COVID-19 using SVM algorithm. Materials and methods: Support Vector Machine (SVM) with sample size = 20 and Decision Tree classification with sample size = 20 was iterated at different times for predicting the accuracy percentage of misinformation about COVID19. The Novel Poly kernel function used in SVM maps the dataset into higher dimensional space which helps to improve accuracy percentage. Results and Discussion: SVM has significantly better accuracy (94.48%) compared to Decision Tree accuracy (93%). There was a statistical significance between SVM and the Decision Tree (p=0.000) (p<0.05 Independent Sample T-test). Conclusion: SVM with Novel Poly kernel helps in predicting with more accuracy the percentage of misinformation about COVID-19.

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“…Indeed, its ability to extract patterns and relations from data has made this research area particularly attractive in tasks involving the description of complex information and dynamics. Successful applications of Deep Learning (DL) (Shorten et al 2021) and Machine Learning (ML) (Nayak et al 2021) techniques in image recognition and segmentation, time series forecasting, sentiment analysis, system control and dynamics simulation are widely present in the literature, as well as robotic self-operating solutions that have proven to be effective in containing social contacts. All these promising outcomes explain the great attention focused on worldwide research on AI as an instrument to fight the COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

et al. 2021

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The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

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Deep Learning applications for COVID-19

Cited by 270 publications

References 99 publications

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

Reliability Evaluation of the Factors That Influenced COVID-19 Patients’ Condition

Comparative Analysis of Identifying Accuracy of Online Misinformation of Covid-19 Using SVM Algorithm with Decision Tree Classification

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

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