1. How can the decision tree model be integrated into clinical practice to assist surgeons in preoperative planning and decision-making?
While the decision tree model may not be a big game-changer in clinical practice, I think it can offer some valuable cautions for surgeons because they may have underestimated certain risks happening in the surgery. Using myself as an example, I experienced anastomotic leak after my surgery because I have had extensive chemotherapy before which makes my healing very slow. This risk was underestimated because my previous treatment was not taken much into account. The decision tree model could incorporate individual patient differences from patient EMR data and conclude different types of operative risks. This allows surgeons to keep certain risks in mind when operating, potentially improving the surgical process.
2. What are the potential benefits and limitations of using this model in a real-world clinical setting?
I think one of the best benefits of decision tree is that it is explainable AI and is very intuitive and visible. The surgeon could see how the model arrived at its conclusion and the criteria for each decision point. This enables the surgeon to better make the decision on what and whether to trust, as opposed to deep learning models that are essentially “black box” and surgeons may find them less credible because they don’t know how the conclusions are arrived at. The limitations is that decision model may have a lower accuracy than more recent and powerful models. It also cannot process textual data such as charting of previous doctors, which limits the types of variables that it is able to take.