The symptoms and side effects of COVID-19 are scattered across a diagnostic spectrum. Some patients are asymptomatic or experience only minor immune responses, while others report significant long-term illnesses lasting complications that may lead to fatal outcomes.

Three researchers from the Georgia Institute of Technology and one from Emory University are trying to help clinicians sort through these factors and the spectrum of patient outcomes by equipping healthcare professionals with a new "decision prioritization tool."

The team's new artificial intelligence-based tool helps clinicians understand and better predict which adverse effects their Covid-19 patients could experience, based on comorbidities and current side effects -; and, in turn, also helps suggest specific Food and Drug Administration-approved (FDA) drugs that could help treat the disease and improve patient health outcomes. The researcher's latest findings are the focus of a new study published October 21 in Scientific Reports.

"Humans are molecular machines, and presumably there are biological and physical rules to dictate our responses," said Skolnick. "We basically built an AI-based approach which was designed given the interactive set of proteins in humans which interact with the [novel] coronavirus," he adds. "We then asked ourselves, 'Could we predict, based on biochemical pathways, which interactive proteins are associated with side effects?'"

Joining Skolnick from the School of Biological Sciences are Ph.D. student Courtney Astore and senior research scientist Hongyi Zhou, both from the Center for the Study of Systems Biology. Joshy Jacob of the Department of Microbiology and Immunology in the Emory Vaccine Center at the Emory School of Medicine also worked on the study.

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World first study used AI tool to predict COVID patients’ oxygen needs | Healthcare IT News — www.healthcareitnews.com

Researchers used anonymized health data from Addenbrooke’s Hospital in Cambridge and 20 other hospitals worldwide.

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department | npj Digital Medicine — www.nature.com

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

Using artificial intelligence to help combat COVID-19 — www.oecd.org

Today, AI technologies and tools play a key role in every aspect of the COVID-19 crisis response. To help facilitate the use of AI throughout the crisis, policymakers should encourage the sharing of medical, molecular, and scientific datasets and models on collaborative platforms to help AI researchers build effective tools for the medical community, and should ensure that researchers have access to the necessary computing capacity. To realize the full promise of AI to combat COVID-19, policymakers must ensure that AI systems are trustworthy and aligned with the OECD AI Principles.