Artificial Intelligence Against Covid-19: An Early Review

Naudé, Wim

doi:10.2139/ssrn.3568314

Cited by 122 publications

(53 citation statements)

References 16 publications

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“…AI in health; offers a wide range of use from community health promotion areas such as air pollution epidemiology, water microbe detection, and aged care and monitoring; to monitoring individuals at risk with suicidal tendencies. Early diagnosis, diagnosis, decision-making, treatment, research, ed-ucation, and preserving and maintaining health are examples of these health areas (31). To make the correct diagnosis and early detection in most cancer screening is still in use.…”

Section: Positive Aspects Of Ai Applications In Healthcare Profession...mentioning

confidence: 99%

Artificial Intelligence in Health Education

Can

Başer²,

Altuntaş³

et al. 2021

SDÜ Tıp Fakültesi Dergisi

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Artificial intelligence (AI) is a field of computer science and engineering with abilities required by human intelligence. One of the most important usage areas of AI is the health sector. From the areas of public health promotion such as air pollution epidemiology, elderly care and monitoring to rapid diagnosis and treatment, from surgery to drug production and neuroscience by analyzing a lot of data from patient records; It offers a wide range of usage opportunities such as monitoring individuals at risk with suicidal tendencies. With AI smart applications, practices such as first aid and resuscitation on patients in the training processes of health professionals have started to be applied more intensively. Strengthening education with these practices provides students with more application opportunities with AI applications in patient follow-up and care services education in health education. Vital signs such as blood pressure, pulse, fever monitoring of virtual patients, virtual vascular access, and blood drawing have become common methods in healthcare professional training. In the training of health professionals, the processes of possible harm to the patients are thus tried to be minimized.

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Section: Positive Aspects Of Ai Applications In Healthcare Profession...mentioning

confidence: 99%

Artificial Intelligence in Health Education

Can

Başer²,

Altuntaş³

et al. 2021

SDÜ Tıp Fakültesi Dergisi

View full text Add to dashboard Cite

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“…Within the short period of 2020, AI has been used to analyse big data of COVID-19 patients to fight the disease [16,17].…”

Section: Artificial Intelligence To Fight Covid-19mentioning

confidence: 99%

“…According to the early review of Naude [17], AI can contribute in six areas to fight against COVID-19: (i) early warnings and alerts, (ii) tracking and prediction, (iii) data dashboards, (iv) diagnosis and prognosis, (v) treatment and cures, and (vi) social control. In this subsection we review the work in some of these areas focusing on social control which relates the most to our work.…”

Section: Artificial Intelligence To Fight Covid-19mentioning

confidence: 99%

A Methodology Based on Deep Q-Learning/Genetic Algorithms for Optimizing COVID-19 Pandemic Government Actions

Miralles-Pechuán

Jiménez

Ponce

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Whenever countries are threatened by a pandemic, as is the case with the COVID-19 virus, governments need help to take the right actions to safeguard public health as well as to mitigate the negative effects on the economy. A restrictive approach can seriously damage the economy. Conversely, a relaxed one may put at risk a high percentage of the population. Other investigations in this area are focused on modelling the spread of the virus or estimating the impact of the different measures on its propagation. However, in this paper, we propose a new methodology for helping governments in planning the phases to combat the pandemic based on their priorities. To this end, we implement the SEIR epidemiological model to represent the evolution of the COVID-19 virus on the population. To optimize the best sequences of actions governments can take, we propose a methodology with two approaches, one based on Deep Q-Learning and another one based on Genetic Algorithms. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system focused on meeting two objectives: firstly, getting few people infected so that hospitals are not overwhelmed, and secondly, avoiding taking drastic measures which could cause serious damage to the economy. The conducted experiments evaluate our methodology based on the accumulated rewards during the established period. The experiments also prove that it is a valid tool for governments to reduce the negative effects of a pandemic by optimizing the planning of the phases. According to our results, the approach based on Deep Q-Learning outperforms the one based on Genetic Algorithms.

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“…However, this approach can not be adapted for our task of fetching relevant explanations for each claim. (Naudé, 2020) and (Bullock et al, 2020) extensively studied the use of machine learning strategies to address various issues regarding COVID-19. The latter also describes fake news and misinformation as a major issue in the ongoing pandemic and further highlights the problem as solving an infodemic.…”

Section: Related Workmentioning

confidence: 99%

Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking

Vijjali,

Potluri,

Kumar

et al. 2020

Preprint

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The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user claims about particular COVID-19 claims. The second model verifies the level of "truth" in the claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.

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Artificial Intelligence Against Covid-19: An Early Review

Cited by 122 publications

References 16 publications

Artificial Intelligence in Health Education

Artificial Intelligence in Health Education

A Methodology Based on Deep Q-Learning/Genetic Algorithms for Optimizing COVID-19 Pandemic Government Actions

Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking

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