IN A NUTSHELL
Stanford big data techno-optimist and internist Russ Altman shows us how the power of machine learning in drug development is helping us understand adverse medication effects.
Professor of Bioengineering, Genetics, Medicine and Computer Science at Stanford University, Russ Altman’s primary research interests are in the application of computing and informatics technologies to problems relevant to medicine. He is particularly interested in methods for understanding drug actions at molecular, cellular, organism and population levels, including how genetic variation impacts drug response. Altman received the U.S. Presidential Early Career Award for Scientists and Engineers, a National Science Foundation CAREER Award and Stanford Medical School's graduate teaching award. He has chaired the Science Board advising the FDA Commissioner, and currently serves on the NIH Director’s Advisory Committee. He is a clinically active internist, the founder of the PharmGKB knowledgebase, and advisor to pharmacogenomics companies.
Web-scale pharmacovigilance: listening to signals from the crowd
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Mining Electronic Records for Revealing Health Data
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Detecting Drug Interactions From Adverse-Event Reports: Interaction Between Paroxetine and Pravastatin Increases Blood Glucose Levels
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Translational Bioinformatics: Linking the Molecular World to the Clinical World
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Data-Driven Prediction of Drug Effects and Interactions
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Discovery and explanation of drug-drug interactions via text mining
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Clinical assessment incorporating a personal genome
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