Researchers at the Icahn School of Medicine at Mount Sinai found that a tool commonly used in research for evaluating a person’s genetic risk for a disease, called a polygenic risk score, was no better at predicting the outcome of a schizophrenia patient’s disease over time than written reports. The results raise important questions about the use of polygenic risk scores in real-world, clinical situations, and also suggest that a doctor’s written report may be an untapped source of predictive information.
A team led by researchers at University of California San Diego School of Medicine has used artificial intelligence technologies to analyze natural language patterns to discern degrees of loneliness in older adults.
Digital cancer registries collect, manage, and store data on cancer patients to help identify trends in diagnoses and treatment. However, cancer pathology reports are complex. To better leverage data, scientists developed an artificial intelligence-based natural language processing tool to help extract information from textual pathology reports.
A team of materials scientists at Lawrence Berkeley National Laboratory – scientists who normally spend their time researching things like high-performance materials for thermoelectrics or battery cathodes – have built a text-mining tool in record time to help the global scientific community synthesize the mountain of scientific literature on COVID-19 being generated every day.
The University of California, Santa Cruz, has established a new master’s (M.S.) degree program in Natural Language Processing (NLP), offered from the UCSC Silicon Valley Campus in Santa Clara. This innovative professional degree program will give students a strong background in the advanced computational technologies used to process and analyze the natural language that humans speak and write.