Researchers from Stanford University have created a data set containing information about how different terms and concepts co-occur in 20 million clinical notes and patient narratives spanning 19 years. Using this data set, scientists can estimate the probability that a patient with a certain condition will take a certain drug or use a certain device. The data set’s creators hope it will help researchers in a wide range of applications, including outcome prediction for medical treatments and analyzing patterns of comorbidity, when patients have two or more conditions.
Data Set Maps Relationship Between Drugs, Diseases, Devices, and Procedures
Travis Korte is a research analyst at the Center for Data Innovation specializing in data science applications and open data. He has a background in journalism, computer science and statistics. Prior to joining the Center for Data Innovation, he launched the Science vertical of The Huffington Post and served as its Associate Editor, covering a wide range of science and technology topics. He has worked on data science projects with HuffPost and other organizations. Before this, he graduated with highest honors from the University of California, Berkeley, having studied critical theory and completed coursework in computer science and economics. His research interests are in computational social science and using data to engage with complex social systems. You can follow him on Twitter @traviskorte.
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