The period directly following surgery is critical for patients as they are at risk of infections and other types of complications, often summarized as severe adverse events (SAE). We hypothesize that impending complications might alter the circadian rhythm and, therefore, be detectable during the night before. We propose a SMOTE-enhanced XGBoost prediction model that classifies nighttime vital signs depending on whether they precede a serious adverse event or come from a patient that does not have a complication at all, based on data from 450 postoperative patients. The approach showed respectable results, producing a ROC-AUC score of 0.65 and an accuracy of 0.75. These findings demonstrate the need for further investigation.