Gregory E. Simon, M.D., M.P.H. (Kaiser Permanente Washington Health Research Institute, Seattle) join Dr. Dixon and Dr. Berezin to discuss the use of machine learning models to analyze electronic health records to predict antidepressant treatment response.

00:00     Introduction 02:31    Focus on practical research 04:55    Population studied 05:57    Predicting outcomes 07:20    Using diagnostic codes, not personalized notes 08:04    What three data items might be more helpful? 08:49    What key indicators are we missing in clinical care? 11:35    A billing tool, not a clinical tool 12:57    Is suicide a predictable event based on electronic health record data? 14:48    “Machine learning and artificial intelligence”  16:15    Methods 18:59     Can we do a better job clarifying what we mean by depression? 22:32    How can we use a predictive model in clinical practice? 28:20    Predictive models, probability, the weather, and communicating 

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