Risk assessment for admitted patients is an important task especially so in times of pandemic when medical institutions and specialists are severely overburdened. Prioritising high-risk patients and deprioritising low-risk ones is of paramount importance to ensure the maximum effectiveness of limited medical resources. Admission reports contain a plethora of information. Combined with radiology reports and laboratory results received during the first 24 hours from admission this information is sufficient for experienced medical professionals to estimate the patient severity. Unfortunately at the peak of pandemic doctors not specialized in respiratory diseases are often forced to make such decisions which can lead to sub-optimal risk assessment and patient routing. In this paper we propose a novel method to predict the risk of death or the need for artificial lung ventilation (ALV) for patients hospitalized with pneumonia or COVID-19 based on their admission reports and possibly other information available in first 24 hours of hospital stay. We do it by applying a Longformer model with a combination of local and global attention, as opposed to more traditional deep learning methods (convolutional neural networks (CNN), recurrent neural networks (RNN) or Transformer models with global attention only). Our proposed approach has the following advantages in comparison with current deep learning - based risk assessment methods. First as it is shown in the paper the Longformer architecture outperforms the baselines including BERT and a combination of BERT and recurrent neural networks.

COVID-19 and its latest omicron strains continue to cause infections across the country as well as globally. Serology (blood) and molecular tests are the two most commonly used methods for rapid COVID-19 testing. Because COVID-19 tests use different mechanisms, they vary significantly. Molecular tests measure the presence of viral SARS-CoV-2 RNA while serology tests detect the presence of antibodies triggered by the SARS-CoV-2 virus. Currently, there is no existing study on the correlation between serology and molecular tests and which COVID-19 symptoms play a key role in producing a positive test result.

The Food and Drug Administration issued a warning Friday to stop using the COVID-19 rapid antigen test Empowered Diagnostics CovClear and the neutralizing antibody rapid test ImmunoPass. These tests are being distributed in the U.S. with labels that show the FDA authorized them, which they did not. The FDA has concerns about the potential risk of false positives when using COVID-19 tests are not approved. They have classified the two tests as a Class I recall, which is the most serious type of recall. "These tests were distributed with labeling showing the FDA authorized them, but neither test has been authorized, cleared, or approved by the FDA for distribution or use in the United States. The FDA is concerned about the potentially higher risk of false results when using unauthorized tests," read an FDA press release.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Last week's White House report reiterated President Biden's employer mandate that businesses with 100 or more employees require every worker to be fully vaccinated for COVID-19 or tested weekly. Jeffrey Zients, the White House COVID-19 response coordinator, summarized in last week's press briefing that, "We are on track to quadruple the supply of rapid, at-home tests available to Americans by December to more than 200 million a month and to increase the number of places Americans can access free testing in the United States to 30,000 community-based locations." He emphasized the president's staunch commitment in adding $1 billion of extra funding already to the recent $2 billion investment to increase supply.

A team of robots is helping researchers expedite the process of analyzing COVID-19 samples. The bots, developed by researchers at the UC Berkeley and UCSF Innovative Genomics Institute (IGI), are being used in a pop-up lab to automate nearly the entire testing process. As noted in a report from the Daily Californian, while one bot takes patient samples from inside to the lab and transfers them to plates with wells another bot conduts what's known as a quantitative polymerase chain reaction, or qPCR, test. According to a report from Forbes, researchers in charge of the team of robots, which have already begun testing samples, say that they're conducting tests on about 200 samples per day. Once the system is scaled up, they hope to reach 1,000 samples per day with a max capacity up to 3,000 tests per day if necessary.