Deep Learning-Based Discrete Calibrated Survival Prediction
This work addresses the need for accurate survival prediction in clinical settings by improving both discrimination and calibration, though it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of survival prediction in medical domains by developing a deep neural network called Discrete Calibrated Survival (DCS), which outperformed competing models in discrimination on three datasets and achieved the best calibration among discrete time models.
Deep neural networks for survival prediction outper-form classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, classical approaches like the Cox Proportional Hazards model display much better calibration, the correct temporal prediction of events of the underlying distribution. Especially in the medical domain, where it is critical to predict the survival of a single patient, both discrimination and calibration are important performance metrics. Here we present Discrete Calibrated Survival (DCS), a novel deep neural network for discriminated and calibrated survival prediction that outperforms competing survival models in discrimination on three medical datasets, while achieving best calibration among all discrete time models. The enhanced performance of DCS can be attributed to two novel features, the variable temporal output node spacing and the novel loss term that optimizes the use of uncensored and censored patient data. We believe that DCS is an important step towards clinical application of deep-learning-based survival prediction with state-of-the-art discrimination and good calibration.