After collaborating with doctors in the intensive care unit at Beth Israel Deaconess Medical Center during her PhD studies, Marzyeh Ghassemi realized that one of their biggest challenges was information overload. So she designed a suite of machine-learning methods to turn messy clinical data into useful predictions about how patients will fare during a hospital stay.
It wasn’t easy. Areas where machine learning excels typically have huge, carefully labeled data sets. Medical data, on the other hand, comes in a bewildering variety of formats at erratic frequencies, ranging from daily written doctors’ notes to hourly blood tests to continuous heart-monitor data.
And while vision and language tasks are innately easy for humans to grasp, even highly trained medical specialists can disagree on diagnoses or treatment decisions. Despite these challenges, Ghassemi developed machine-learning algorithms that take diverse clinical data and accurately predict things like how long patients will stay in the hospital, how likely they are to die while there, and whether they’ll need interventions such as blood transfusions or ventilators.
This fall Ghassemi joins the University of Toronto and the Vector Institute, where she’s hoping to test her algorithms at local hospitals.