S6: Intelligent Computing Solutions for SARS-CoV-2 Covid-19 (INClutions COVID-19)

Carmen Paz Suárez Araujo

Juan Luis Navarro Mesa

Universidad de Las Palmas de Gran Canaria, Spain

Abstract

The World Health Organization (WHO) declared novel coronavirus 2019 (COVID- 19), an infectious epidemic caused by SARS-COV-2, as Pandemic in March 2020. It has caused a worldwide sudden and substantial increase in hospitalizations for pneumonia with multi-organ disease; affecting more than 120 million people in 218 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate, posing strain on the healthcare systems.

As the early prediction can reduce the spread of the virus, there is an urgent need for efficient early diagnosis and prognosis of COVID-19. Furthermore, taking into account the limited data sources with expert labeled data for COVID-19 detection are insufficient, manual detection of infections is time consuming, is highly desirable to have intelligent prediction and diagnosis tools to improve these diagnostic and prognostic processes. In addition to that, the inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of virus.

The digitalization of health data, together with the advent of advanced data mining, big data, intelligent computing and machine learning, has brought forward the promising possibility of introducing intelligent technology in medicine. This framing emphasizes that Artificial Intelligence (AI) is in the essence of the fundamental technology required to meaningfully process data even those showing so minute differences that exceed the capacity of the clinicians to be detected or analysed.

For the advancement in this very important field there are four major aspects we would like consider in INClutions COVID-19: diagnosis, prognosis, treatment strategies and biological perfil of course of COVID-19.

We propose a scientific session with a high inter/multidisciplinary character. AI and clinical disciplines working together, keeping a continuous interrelation between them. The methodology and main developments of papers to include in this session must belong to AI, especially, intelligent computing, machine learning, deep learning, intelligent data mining, big data, metaheuristic techniques, bio-signal and medical image processing, neural computing-based systems, intelligent systems among other.