Article here via Futurism…
“A new paper published in Nature suggests that feeding electronic health record data to a deep learning model could substantially improve the accuracy of projected outcomes. In trials using data from two U.S. hospitals, researchers were able to show that these algorithms could predict a patient’s length of stay and time of discharge, but also the time of death.
The neural network described in the study uses an immense amount of data, such as a patient’s vitals and medical history, to make its predictions. A new algorithm lines up previous events of each patient’s records into a timeline, which allowed the deep learning model to pinpoint future outcomes, including time of death. The neural network even includes handwritten notes, comments, and scribbles on old charts to make its predictions. And all of these calculations in record time, of course.”
Why It’s Hot
What if we could use this technology to help drive patient identification (a key goal across GSK respiratory)? By syncing with EHR’s, HCPs would be able to identify the frequent exacerbators, pinpoint when their next exerbation may be and take preventative steps to avoid potentially deadly COPD or Severe Asthma exacerbations.