New York, Aug 30 : A team of global researchers has developed an innovative patient triage platform powered by artificial intelligence (AI) that the researchers say is capable of predicting patient disease severity and length of hospitalisation during a viral outbreak. The platform, which leverages machine learning and metabolomics data, is intended to improve patient management and help health care providers allocate resources more efficiently during severe viral outbreaks that can quickly overwhelm local health care systems. Metabolomics is the study of small molecules related to cell metabolism. 'Being able to predict which patients can be sent home and those possibly needing intensive care unit admission is critical for health officials seeking to optimise patient health outcomes and use hospital resources most efficiently during an outbreak,' said Vasilis Vasiliou, a professor of epidemiology at Yale School of Public Health (YSPH), Yale University. The platform developed using Covid-19 as a disease model integrates routine clinical data, patient comorbidity information, and untargeted plasma metabolomics data to drive its predictions. It is detailed in the journal Human Genomics. "Our AI-powered patient triage platform is distinct from typical Covid-19 AI prediction models,' said lead author Georgia Charkoftaki, associate research scientist in the Department of Environmental Health Sciences at YSPH. 'It serves as the cornerstone for a proactive and methodical approach to addressing upcoming viral outbreaks." Using machine learning, the researchers built a model of Covid-19 severity and prediction of hospitalisation based on clinical data and metabolic profiles collected from patients hospitalised with the disease. 'The model led us to identify a panel of unique clinical and metabolic biomarkers that were highly indicative of disease progression and allows the prediction of patient management needs very soon after hospitalisation,' the researchers wrote in the study. For the study, the research team collected comprehensive data from 111 Covid-19 patients admitted to hospital during a two-month period in 2020 and 342 healthy individuals (health care workers) who served as controls. The patients were categorised into different classes based on their treatment needs, ranging from not requiring external oxygen to requiring positive airway pressure or intubation. The study identified a number of elevated metabolites in plasma that had a distinct correlation with Covid-19 severity. They included allantoin, 5-hydroxy tryptophan, and glucuronic acid. Notably, patients with elevated blood eosinophil levels were found to have a worse disease prognosis, exposing a potential new biomarker for Covid-19 severity. The researchers also noted that patients who required positive airway pressure or intubation exhibited decreased plasma serotonin levels, an unexpected finding that they said warrants further research. /IANS