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Stratifying patients at risk for postoperative complications may facilitatetimely and accurate workups and reduce the burden of adverse events on patientsand the health system. Currently, a widely-used surgical risk calculatorcreated by the American College of Surgeons, NSQIP, uses 21 preoperativecovariates to assess risk of postoperative complications, but lacks dynamic,real-time capabilities to accommodate postoperative information. We propose anew Hidden Markov Model sequence classifier for analyzing patients'postoperative temperature sequences that incorporates their time-invariantcharacteristics in both transition probability and initial state probability inorder to develop a postoperative "real-time" complication detector. Data fromelective Colectomy surgery indicate that our method has improved classificationperformance compared to 8 other machine learning classifiers when using thefull temperature sequence associated with the patients' length of stay.Additionally, within 44 hours after surgery, the performance of the model isclose to that of full-length temperature sequence.

Early Stratification of Patients at Risk for Postoperative Complications after Elective Colectomy