A major focus of our research has been in the intensive care unit (ICU) where we have been
developing advanced machine learning algorithms capable of meaningfully summarizing large volumes of continuously
measured patient data (e.g., vital signs time series, lab values, medication records, clinical notes, etc.),
with the goal of timely prediction of potentially life-threatening clinical events (e.g., Sepsis).
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We also have a number of active collaborations at the intersection of machine learning and neuroscience/psychiatry. We believe computational and quantitative approaches to phenotyping clinical states of patients (e.g., during treatment of depression), can prove useful for both monitoring outcomes and justifying treatment interventions. (Find out more)
Our team is also actively involved in extending our monitoring and predictive algorithms beyond the inpatient setting, using ambulatory and wearable technologies, where handling of noise and recording artifacts requires novel signal processing techniques. (Find out more)