Machine Learning Support for Emergency Triage of Pulmonary Collapse in COVID-19

In emergency rooms across the world, doctors facing bed shortages must decide if patients with suspected or confirmed COVID-19 are safe to go home or need hospital-level monitoring. The current state of medical knowledge is failing here: some patients in the hospital ultimately do not require advanced care, wasting beds; others are sent home, only to deteriorate rapidly. Our goal is to produce an algorithm that helps physicians make better triage decisions, by predicting pulmonary collapse on the basis of X-rays that nearly all patients with respiratory complaints get in the ER. Our in-depth discussions with frontline doctors treating COVID-19 have identified this as an area of genuine need. And thanks to our close relationship with one of the largest healthcare systems in the Northwest, we already have a signed agreement in place, with access to 4 million chest X-rays linked to physiological markers of pulmonary collapse: acute respiratory distress syndrome (ARDS), the "final common pathway" for many infections including COVID-19, hypoxia, and mortality from linked Social Security data. This enables the modern machine learning toolkit to be deployed, and complements our own collective expertise in medical decision making, machine learning, and understanding of healthcare systems and behavior. If successful, we will deploy the algorithm in our partner's 51 hospital-based ERs. More broadly, our work is a general prediction toolkit for pulmonary collapse, meant to transfer across healthcare systems. We will thus provide pro bono consultation to health systems wishing to integrate the tool, and open-source the prototype algorithms we develop.