Computational model to track flu using Twitter data

An international team led by Alessandro Vespignani from Northeastern University has developed a computational model to predict the spread of the flu in real time. This unique model uses posts on Twitter in combination with key parameters of each season's epidemic, including the incubation period of the disease, the immunization rate, how many people an … Continue reading Computational model to track flu using Twitter data

Collaboration between UCSF, Intel to develop deep learning analytics for healthcare

UC San Francisco's Center for Digital Health Innovation (CDHI) today announced a collaboration with Intel Corporation to deploy and validate a deep learning analytics platform designed to improve care by helping clinicians make better treatment decisions, predict patient outcomes, and respond more nimbly in acute situations.The collaboration brings together Intel's leading-edge computer science and deep … Continue reading Collaboration between UCSF, Intel to develop deep learning analytics for healthcare

Researchers use multi-task deep neural networks to automatically extract data from cancer pathology reports

Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs--a national network of organizations that systematically collect demographic and … Continue reading Researchers use multi-task deep neural networks to automatically extract data from cancer pathology reports

Researchers identify suicidal behavior using machine learning algorithm on patients verbal and non-verbal data

A new study shows that machine learning is up to 93 percent accurate in correctly classifying a suicidal person and 85 percent accurate in identifying a person who is suicidal, has a mental illness but is not suicidal, or neither. These results provide strong evidence for using advanced technology as a decision-support tool to help … Continue reading Researchers identify suicidal behavior using machine learning algorithm on patients verbal and non-verbal data