  Stratifying patients at risk for postoperative complications may facilitate
timely and accurate workups and reduce the burden of adverse events on patients
and the health system. Currently, a widely-used surgical risk calculator
created by the American College of Surgeons, NSQIP, uses 21 preoperative
covariates to assess risk of postoperative complications, but lacks dynamic,
real-time capabilities to accommodate postoperative information. We propose a
new Hidden Markov Model sequence classifier for analyzing patients'
postoperative temperature sequences that incorporates their time-invariant
characteristics in both transition probability and initial state probability in
order to develop a postoperative "real-time" complication detector. Data from
elective Colectomy surgery indicate that our method has improved classification
performance compared to 8 other machine learning classifiers when using the
full temperature sequence associated with the patients' length of stay.
Additionally, within 44 hours after surgery, the performance of the model is
close to that of full-length temperature sequence.
