After surgery, pain is expected and often subsides within a few weeks. But for around 20% of patients, persistent pain may continue for months or even years after a procedure, impacting quality of life and putting patients at risk for opioid overuse. Symptoms vary widely in severity and in the type of pain experienced, making […]
Roughly 4% of the population is affected by a congenital brain malformation that has eluded researchers’ efforts to find causes and treatments. For the condition, Chiari type-1 malformation, the diagnosis is straightforward: the lower part of the brain, known as the cerebellum, protrudes at least five millimeters through the gap in the skull that connects […]
Researchers who had been using Fitbit data to help predict surgical outcomes have a new method to more accurately gauge how patients may recover from spine surgery. Using machine-learning techniques developed at the AI for Health Institute at Washington University in St. Louis, Chenyang Lu, PhD, the Fullgraf Professor at the university’s McKelvey School of Engineering, collaborated with Jacob […]
Depression and anxiety are among the most common mental health disorders in the United States, but more than half of people struggling with the conditions are not diagnosed and treated. Hoping to find simple ways to detect such disorders, mental health professionals are considering the role of popular wearable fitness monitors in providing data that […]
Over the past several years, managing one’s mental health has become more of a priority with an increased emphasis on self-care. Depression alone affects more than 300 million people worldwide annually. Recognizing this, there is significant interest to leverage popular wearable devices to monitor an individual’s mental health by measuring markers such as activity levels, […]
Hospitals spend about one-third of their expenses on perioperative care – the high-stakes period just before and after a patient is in surgery — to prevent complications afterward. Washington University in St. Louis researchers have developed a machine learning approach that exploits the large amount of clinical data collected during perioperative care to predict potential […]