Introduction Surgeons intraoperative decisions significantly impact patient outcomes. In the model described by Cristancho et al interoperative decisions are guided by probabilistic reasoning, informed by the evolving intraoperative features. This paper aims to compare the utility of a traditional logistic regression (LR) model for critical view of safety (CVS) achievement to BN maps using intraoperative features. It hypothesises that BN mapping better integrates with surgeon heuristics. Methods Using prospectively gathered intraoperative data Bayesian network (BN) maps were developed and tested for their ability to predict critical view of safety achievement. Performance was compared to traditional logistic regression models to consider their utility in practice. Results In total 4663 patients were identified. Of these patients 2837 (61%) presented acutely and 3122 (67%) were female. The CVS was achieved in 4131 (92%) of patients. In total four BN were developed. Optimal performance was seen in model 2 with an AUC of 0.879(0.798-0.960)(P<0.001). Selecting cut off of 0.6 gave an optimised sensitivity of 99% and a specificity of 45% for CVS achievement. In comparison to this the combined acute LR model. ROC curve analysis gave an AUC of 0.829 (0.787 – 0.872 ) (p < 0.001) . A cut off of 75% probability resulted in a sensitivity of 95% and a specificity of 38% for CVS achievement. Conclusion The present study illustrates how BN modelling can map to surgeon decision making to facilitate reasoning in complex environments. Further work is needed to facilitate data capture and implementation. Despite this they represent a promising avenue for intuitive decision support tools.