An electronic nudge to clinicians—triggered by an algorithm that used machine learning methods to flag patients with cancer who would most benefit from a conversation around end-of-life goals—tripled the rate of those discussions.
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.
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.
Rutgers researchers say gender bias and stereotypes corresponding to certain occupations are prevalent on digital and social media platforms.
New research from a team of Computing and Information Science scholars at Cornell University raises questions about hiring algorithms and the tech companies who develop and use them: How unbiased is the automated screening process? How are the algorithms built? And by whom, toward what end, and with what data?
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.”
An algorithm developed by faculty at The University of Texas Health Science Center at Houston (UTHealth) can help physicians outside of major stroke treatment centers assess whether a patient suffering from ischemic stroke would benefit from an endovascular procedure to remove a clot blocking an artery.