Using a patient’s entire chart, Google’s deep learning methods were able to predict, 24 hours after admission, the risk of that patient’s death at 19.9% while the regular hospital’s computers predicted that risk at 9.3%. The patient died 10 days after admission.
Google’s researchers worked with others from UC San Francisco, Stanford Medicine, and The University of Chicago Medicine. Their new algorithms accurately predicted several medical events based on data gleaned from de-identified electronic health records (EHR), says the retrospective study published in npj Digital Medicine. The algorithms analyzed more than 46 billion data points, including clinical notes for 216,221 adult patients hospitalized for at least 24 hours in two U.S. academic medical centers. In addition to in-hospital mortality, the AI was able to predict 30-day unplanned readmissions and patients’ final discharge diagnoses better than traditional clinically-used predictive models.