Plug-and-play use of tree-based methods: consequences for clinical prediction modeling

In healthcare, statistical models can be used to predict a patient's risk of having or developing certain medical conditions. These predictions can be used for diagnosis, treatment planning, or informing patients. While there are many ways to build these models, a method that uses decision trees (and collections of these trees like random forest and XGBoost) has become increasingly popular. In our work, we used simulations and real patient data, to illustrate how these tree-based models learn from data. We show that these models inherently learn associations that are often biologically unrealistic, even when a lot of data are available. For example, when a predictive variable (e.g., body mass index) increases, we would expect smooth, gradual changes in risk. Instead, tree-based models tend to make sudden, unrealistic jumps or unexpected drops in their predictions. To discuss why this is relevant, we examined a case study in radiotherapy, where doctors use models to predict treatment side effects, based on variables such as the planned radiation dose. Here, the behavior of tree-based models sometimes leads to predictions where a tiny increase in radiation dose would either suddenly decrease the risk of side effects (which is not biologically plausible) or cause a dramatic increase in risk. Such biologically implausible predictions could result in a lack of trust from clinicians and potentially lead to suboptimal treatment decisions. While tree-based models are powerful methods, we argue that researchers should be aware of how these models learn and carefully evaluate whether this is acceptable for each specific clinical application. We also illustrate some promising solutions, such as forcing the models to incorporate existing medical knowledge, for example, by requiring that higher radiation doses must result in higher risks of side effects. Our work helps researchers understand the limitations and potential of these increasingly popular tree-based prediction methods and support their responsible use.