How Cedars-Sinai Predicts Number of COVID-19 Patients

When the novel coronavirus started spreading across the U.S., hospital leaders were faced with a unique challenge: How could they accurately forecast the number of patients who would need hospitalization when no one knew what to expect from this new disease? To answer this and other questions, the data science team at Cedars-Sinai developed a machine learning platform to predict staffing needs. The team adjusted the platform’s algorithms to forecast data points related to the novel coronavirus. Now the platform tracks local hospitalization volumes and the rate of confirmed COVID-19 cases, running multiple forecasting models to help anticipate and prepare for increasing COVID-19 patient volumes with an 85%-95% degree of accuracy.

Deep learning algorithm identifies tumor subtypes based on routine histological images

Researchers at the University of Chicago Medicine Comprehensive Cancer Center, working with colleagues in Europe, created a deep learning algorithm that can infer molecular alterations directly from routine histology images across multiple common tumor types. The findings were published July 27 in Nature Cancer.

World-Leading Microscopes Take Candid Snapshots of Atoms in Their ‘Neighborhoods’

Scientists at Berkeley Lab have demonstrated how a powerful electron microscopy technique can provide direct insight into the performance of any material – from strong metallic glass to flexible semiconducting films – by pinpointing specific atomic “neighborhoods.”