A collaborative effort between University of Delaware (UD) researchers and Christiana Care Health System clinicians uses merged electronic health records from various institutions to enhance the coordination of care and clinical results for patients with chronic kidney disease.
The researchers are employing longitudinal data culled from a large pool of patients to initially make predictions concerning hospitalization patterns and later anticipate other trends in the disease.
UD professor Hagit Shatkay says a probabilistic model for hospitalization based on the data mirrors various factors, including trends in blood pressure and laboratory results, changes in medications, and the frequency of outpatient visits and phone calls. "These rich and diverse data require that we develop and examine machine learning-based methods for representation of, and prediction from, such data," Shatkay notes.
The researchers' approach involves identifying the features that carry the most information about hospitalization so a more compact representation of the data can be generated, while also concentrating on interdependencies among the various measurements to boost predictive efficiency.
Shatkay believes the approach her team is developing can be applied on a much broader scale. "We're figuring out ways to sift through these massive amounts of data and determine what's relevant and what's not," she says.
From UDaily (DE)
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