# Using Explainable Machine Learning to Explore the Impact of Synoptic Reporting on Prostate Cancer
## 4 Discussion
For example, we can see that the number of patients that are 50 years old or younger is lower than in other age groups. We can also observe that there are more patients with an EAU of 2 or 3 (i.e., intermediate and high risk, respectively; both localised) compared to the rest. A more interesting practical application was to provide additional information on how the models deal with their inputs. Particularly, we can clearly see how the CPH approach is only capable of modelling linear relations between the features and its output. It could be argued that this model could be extended [66; 67; 68] to account for non-linearities in the data through, for example, covariate transformations, step-wise regression, or specialized functions. However, these alternatives are often highly dependent on the (subjective) expertise of the researcher, which could also reduce the model generalizability, making it prone to overfitting. In contrast, the XGB approach is capable of capturing and modelling said non-linearities out-of-the-box, without any additional effort from the researcher in a more generalizable, data-driven approach. For example, in the case of age (Figure 4A), we can identify a plateau for patients between \(\sim\)40 and \(\sim\)55 years on their SHAP values. Then, there is a relatively constant rise until \(\sim\)75 years, with a sharp increase after that. Moreover, dependence plots also allow to compare the model's inner workings with real life and clinical intuition (based on the user's medical expertise) to better understand the phenomenon at hand. For example, EAU (Figure 4B) shows that the XGB model gives patients with a low risk a much better chance of survival compared to the rest, which is expected. Lastly, we can see that the XGB model found that when a patient received SR (Figure 4C), his SHAP values shifted from positive to negative, further suggesting that SR has a positive impact on predicted patient survival. Since the latter was of particular interest, we also computed the interaction effects SR had with age and EAU (Figure 5). Figure 5A reveals that the negative/positive effect of not receiving/receiving SR is more impactful for older patients. Figure 5B shows a similar trend: not receiving/receiving SR is more detrimental/beneficial for patients that are in a higher EAU risk group (either localised or locally advanced). The results presented here are the first step in exploring the effect that SR has on the survival of prostate cancer patients. They should be expanded and confirmed with further research. For example, it could be interesting to incorporate additional prognostic factors (such as patient performance or comorbidities) and look at cause-specific survival (which were not available in the dataset at hand). It could also be valuable to investigate what items were missing in the narrative reports in comparison with those with SR and analyze what impact they had on the patient care pathway. Given the volume of reports, using natural language processing (NLP) could be a suitable approach [69]. Lastly, it is worth mentioning that (X)ML-based models still present a few limitations. For instance, they are well-known to be data hungry, requiring large amounts of data to achieve acceptable performance. Very often, they require optimization and tuning of different parameters, which can be a cumbersome process. Lastly, depending on their complexity, these type of models can be very resource consuming and computationally demanding.