Bigdata fog based cyber physical system for classifying, identifying and prevention of SARS disease

Raghav, R. S.; Dhavachelvan, P.

doi:10.3233/jifs-169992

Cited by 7 publications

(5 citation statements)

References 14 publications

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“… ➢ In 2019, a recurrent neural network and decision tree-based model was proposed for detection of SARS. The system was able to identify cases using user-generated text and geospatial information with 91% accuracy 122 . Prognosis of illness and response to treatment ➢ No relevant studies were identified.…”

Section: Resultsmentioning

confidence: 99%

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

et al. 2021

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Artificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

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Section: Resultsmentioning

confidence: 99%

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

et al. 2021

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“…The system has used the concept of population density of infected and uninfected individuals in a particular area to ascertain the risk level of infection in those areas. Raghav and Dhavachelvan [30] had proposed a fog-based cyber physical system for remotely diagnosing the severe acute respiratory syndrome cronavirus (SARS-CoV) and analyzing its outbreak. The system has employed J48 decision tree for classifying the type of infection and big data-based approach MongoDB for storing, modeling, analyzing and visualizing the SARS outbreak.…”

Section: Infectious Diseases-based Intelligent Healthcarementioning

confidence: 99%

An intelligent healthcare system for predicting and preventing dengue virus infection

Sood

Mahajan

et al. 2021

Computing

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“…Big data were also used in healthcare service delivery for classifying, identifying and preventing SARS disease. This further stresses role of MCPS in the healthcare domain to fetch deep insights about the nature of the disease and carry the monitoring process with early detection of infected patients (Raghav and Dhavachelvan, 2019). Social robots are used to interact with patients to take care of the psychological ill effects of illness, though it is in the initial stage of development in health domains they will have the ability to engage patients on social and emotional dimensions (Breazeal, 2011;Henschel et al, 2021).…”

Section: Mcps Impact On Comprehensiveness Dimension Of Health Service...mentioning

confidence: 99%

“…The effectiveness of the use of MCPS in healthcare service delivery is found in both physical and mental illnesses. In physical illness, MCPS is used effectively in dealing with high-risk pregnancy (Wrobel et al, 2015), respiratory illness, Asthma (Naranjo-Hern andez et al, 2018;Tomasic et al, 2018), serious old age-related illness (Loria, 2021;Shah et al, 2016;Walker et al, 2019), injuries (Peterson, 2018), infectious diseases (Raghav and Dhavachelvan, 2019;Wu et al, 2021) and so on. In mental health, telehealth technologies are used to conduct psychological assessments and counselling.…”

Section: Mcps Impact On Quality and Person-centredness Dimensions Of ...mentioning

confidence: 99%

The impact of medical cyber–physical systems on healthcare service delivery

Sony

Antony

McDermott

2022

TQM

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PurposeThe pandemic has reinforced the need for revamping the healthcare service delivery systems around the world to meet the increased challenges of modern-day illnesses. The use of medical cyber–physical system (MCPS) in the healthcare is one of the means of transforming the landscape of the traditional healthcare service delivery system. The purpose of this study is to critically examine the impact of MCPS on the quality of healthcare service delivery.Design/methodology/approachThis paper uses an evidence-based approach, the authors have conducted a systematic literature review to study the impact of MCPS on healthcare service delivery. Fifty-four articles were thematically examined to study the impact of MCPS on eight characteristics of the healthcare service delivery proposed by the world health organisation.FindingsThe study proposes support that MCPS will positively impact (1) comprehensiveness, (2) accessibility, (3) coverage, (4) continuity, (5) quality, (6) person-centredness, (7) coordination, (8) accountability and (9) efficiency dimension of the healthcare service delivery. The study further draws nine propositions to support the impact of MCPS on the healthcare service delivery.Practical implicationsThis study can be used by stakeholders as a guide point while using MCPS in healthcare service delivery systems. Besides, healthcare managers can use this study to understand the performance of their healthcare system. This study can further be used for designing effective strategies for deploying MCPS to be effective and efficient in each of the dimensions of healthcare service delivery.Originality/valueThe previous studies have focussed on technology aspects of MCPS and none of them critically analysed the impact on healthcare service delivery. This is the first literature review carried out to understand the impact of MCPS on the nine dimensions of healthcare service delivery proposed by WHO. This study provides improved thematic awareness of the resulting body of knowledge, allowing the field of MCPS and healthcare service delivery to progress in a more informed and multidisciplinary manner.

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Bigdata fog based cyber physical system for classifying, identifying and prevention of SARS disease

Cited by 7 publications

References 14 publications

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

An intelligent healthcare system for predicting and preventing dengue virus infection

The impact of medical cyber–physical systems on healthcare service delivery

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