Intelligent system for COVID-19 prognosis: a state-of-the-art survey

Nayak, Janmenjoy; Naik, Bighnaraj; Dinesh, Paidi; Vakula, Kanithi; Rao, B. Kameswara; Ding, Weiping; Pelusi, Danilo

doi:10.1007/s10489-020-02102-7

Cited by 44 publications

(25 citation statements)

References 119 publications

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“…Nasajpour et al [ 23 ] discuss several IoT healthcare applications during three main phases: early diagnosis, quarantine time, and after recovery. A recent survey [ 24 ] discusses the use of Machine Learning (ML), AI and other intelligent approaches for the prognosis of COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT

Umair

Cheema

Cheema³

et al. 2021

Sensors

156

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COVID-19 has disrupted normal life and has enforced a substantial change in the policies, priorities and activities of individuals, organisations and governments. These changes are proving to be a catalyst for technology and innovation. In this paper, we discuss the pandemic’s potential impact on the adoption of the Internet of Things (IoT) in various broad sectors, namely healthcare, smart homes, smart buildings, smart cities, transportation and industrial IoT. Our perspective and forecast of this impact on IoT adoption is based on a thorough research literature review, a careful examination of reports from leading consulting firms and interactions with several industry experts. For each of these sectors, we also provide the details of notable IoT initiatives taken in the wake of COVID-19. We also highlight the challenges that need to be addressed and important research directions that will facilitate accelerated IoT adoption.

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Section: Related Workmentioning

confidence: 99%

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT

Umair

Cheema

Cheema³

et al. 2021

Sensors

156

View full text Add to dashboard Cite

show abstract

“…The above literature has given model examples that suggests the use of Bayesian networks in healthcare sector for detecting and differentiating between different and complex medical conditions. Previous work for detecting COVID-19 has focused on different machine learning tools except Bayesian networks even though Bayesian networks are very powerful in reasoning under uncertainty [22], [23], [24]. In this work, we explore the feasibility of using the probabilistic graphical models in the form of Bayesian networks to build an effective diagnosis system for controlling and detecting COVID-19 disease.…”

Section: Background Informationmentioning

confidence: 99%

Detecting COVID-19 Utilizing Probabilistic Graphical Models

Alsuwat¹,

Alzahrani²,

Alsuwat³

2021

IJACSA

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Probabilistic graphical models are employed in a variety of areas such as artificial intelligence and machine learning to depict causal relations among sets of random variables. In this research, we employ probabilistic graphical models in the form of Bayesian network to detect coronavirus disease 2019 (denoted as COVID-19) disease. We propose two efficient Bayesian network models that are potent in encoding causal relations among random variable, i.e., COVID-19 symptoms. The first Bayesian network model, denoted as BN1, is built depending on the acquired knowledge from medical experts. We collect data from clinics and hospitals in Saudi Arabia for our research. We name this authentic dataset DS covid . The second Bayesian network model, denoted as BN2, is learned from the real dataset DS covid depending on Chow-Liu tree approach. We also implement our proposed Bayesian network models and present our experimental results. Our results show that the proposed approaches are capable of modeling the issue of making decisions in the context of COVID-19. Moreover, our experimental results show that the two Bayesian network models we propose in this work are effective for not only extracting casual relations but also reducing uncertainty and increasing the effectiveness of causal reasoning and prediction.

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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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Intelligent system for COVID-19 prognosis: a state-of-the-art survey

Cited by 44 publications

References 119 publications

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT

Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT

Detecting COVID-19 Utilizing Probabilistic Graphical Models

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

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