Editor's Note
Using machine learning on electronic health record (EHR) postoperative data linked to the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) outcomes data, researchers developed a model with 163 predictors of postoperative complications at the University of Colorado Hospital.
Of 6,840 patients analyzed with the model, 13.5% had at least one of the 18 complications tracked by ACS NSQIP. The model had 88% specificity, 83% sensitivity, and an area under the curve of 0.93.
This model may be useful for electronic surveillance of postoperative complications, the authors say.
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