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Swarm Learning AI Detects COVID-19, Cancers, and More


A new anti-COVID-19 AI is claimed to be having features that can detect cancers, lung diseases, and other serious illnesses. As of the moment, the global coronavirus pandemic is still affecting various countries across the globe. The health crisis is still preventing some people from going out and doing office-based works since they don't know who is infected with the deadly COVID-19. Because of this, some international researchers and health experts decided to create a new AI called Swarm Learning. They claimed that this new artificial intelligence can identify if a person is infected by the viral coronavirus.

Detection of COVID -- 19 using Deep Learning


"Coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2". "The disease first originated in December 2019 from Wuhan, China and since then it has spread globally across the world affecting more than 200 countries. The impact is such that the World Health Organization(WHO) has declared the ongoing pandemic of COVID-19 a Public Health Emergency of International Concern." As of 29th April, there are a total of 31,30,191 cases with 2,17,674 deaths in more than 200 countries across the world. So, in this particular scenario, one primary thing that needs to be done and has already started in the majority of the countries is Manual testing, so that the true situation can be understood and appropriate decisions can be taken.

Identification of long COVID patients through machine learning


In a recent study posted to Preprints with The Lancet*, researchers developed a machine learning approach to identify patients with long coronavirus disease (COVID). The post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are called long COVID. In the present study, researchers aimed to generate a robust clinical definition for long COVID using data related to long COVID patients. The team utilized data obtained from electronic health records that were integrated and harmonized in the secure N3C Data Enclave. This allowed the team to identify unique patterns and clinical characteristics among COVID-19-infected patients.

Data science in a post-COVID world


I am often asked about the state of data science and where we sit now from a maturity perspective. The answer is pretty interesting, especially now that it's been more than a year since COVID-19 rendered most data science models useless -- at least for a time. COVID forced companies to make a full model jump to match the dramatic shift in daily life. Models had to be rapidly retrained and redeployed to try to make sense of a world that changed overnight. Many organizations ran into a wall, but others were able to create new data science processes that could be put into production much faster and easier than what they had before.

Investigators Identify Characteristics to Better Define Long COVID


Investigators have identified characteristics of individuals with long COVID and those who are likely to have it by using machine learning techniques. The investigators, who were supported by the National Institutes of Health (NHI), analyzed a collection of electronic health records (EHR) available for COVID-19 research to help better identify who has long COVID. Investigators used the EHR data, from the National COVID Cohort Collaborative (N3C), a centralized national public database led by the NIH's National Centers for Advancing Translation Sciences, to identify more than 100,000 likely cases of long COVID, as of October 2021 and 200,000 cases as of May 2022. "It made sense to take advantage of modern data analysis tools and a unique big data resource like N3C, where many features of long COVID can be represented," Emily Pfaff, PhD, a clinical informaticist at the University of North Carolina at Chapel Hill, said in a statement. The N3C data includes information representing more than 13 million individuals nationwide and nearly 5 million positive COVID-19 cases.