Rocky 2022

Machine learning methods that can capture complex and non-linear relationships are a useful approach to accurately predict time-to-event outcomes in biomedical research. These methods are often considered “black-box” algorithms that are not interpretable and therefore difficult to trust in making important clinical decisions. Explainable machine learning proposes the use of explainers that can be applied to predictions from any complex model to provide insight into how the model arrived at that prediction. These explainers describe how a patient’s characteristics are contributing to their prediction. The application of explainers to survival prediction models can provide explanations for an individual’s overall survival curve as well as survival predictions at particular follow-up times. Here, we present a novel visualization technique, a “picket fence plot”, to display and explore individual-specific explanations. We integrated the picket fence plot into an interactive R/Shiny application that can load predicted survival curves and model explanations and supports comparison between models and patients. We demonstrate an application of our tool to explain prostate cancer-specific survival predictions from a random survival forest built using data from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.