The beginning of the COVID-19 pandemic presented a huge challenge to healthcare workers. Doctors struggled to predict how different patients would fare under treatment against the novel SARS-CoV-2 virus. Deciding how to triage medical resources when presented with very little information took a mental and physical toll on caregivers as the pandemic progressed.
To ease this burden, researchers at Pacific Northwest National Laboratory (PNNL), Stanford University, Virginia Tech, and John Snow Labs developed TransMED, a first-of-its-kind artificial intelligence (AI) prediction tool aimed at addressing issues caused by emerging or rare diseases.
“As COVID-19 unfolded over 2020, it brought a number of us together into thinking how and where we could contribute meaningfully,” said chief scientist Sutanay Choudhury. “We decided we could make the most impact if we worked on the problem of predicting patient outcomes.”
“COVID presented a unique challenge,” said Khushbu Agarwal, lead author of the study published in Nature Scientific Reports. “We had very limited patient data for training an AI model that could learn the complex patterns underlying COVID patient trajectories.”
The multi-institutional team developed TransMED to address this challenge, analyzing data from existing diseases to predict outcomes of an emerging disease.