Safe Continual Learning for Sepsis Early Detection - Project Summary: Sepsis is a life-threatening condition characterized by a dysregulated immune response to infection, leading to systemic inflammation and multi-organ dysfunction. Despite advances in medical care, sepsis remains a significant cause of morbidity and mortality worldwide, with early detection and intervention being critical to improving patient outcomes. However, the early detection of sepsis poses several challenges due to its heterogeneous presentation, non-specific symptoms, and the dynamic nature of patient populations and clinical practices. Traditional machine learning approaches have shown promise in addressing these challenges, but their performance may degrade over time as the underlying data distribution changes, necessitating periodic model retraining. Continual learning is a learning paradigm that enables models to adapt to new data continually without forgetting previously learned knowledge. This approach is particularly well-suited to the early detection of sepsis, as it allows models to capture the evolving nature of the condition, incorporate new diagnostic tools and biomarkers, and provide personalized risk assessments. Despite its advantages, continual learning can incur several safety issues in sepsis early detection. First, periodic retraining may lead to catastrophic forgetting, which is a phenomenon that occurs when a machine learning model, upon excessive adaptation to new data during retraining, loses its previously acquired knowledge from the original training data. Second, as models are retrained with new data, there is a risk of overfitting to the most recent data, which may reduce their generalizability to future patient populations or clinical settings. Ensuring a balance between updating the model to adapt to changing conditions and maintaining generalizability is crucial for maintaining safety. The overarching goal of this project is to develop safe continual learning methods for early sepsis detection that can adapt to new data while avoiding catastrophic forgetting and maintaining generalization. First, we will develop methods that update a small subset of weights in the existing model based on the new data, thereby preserving the previously learned knowledge while adapting to the new information and leading to a more robust and adaptive model for early sepsis detection. This will allow us to test the hypothesis that avoiding catastrophic forgetting in continual learning will increase early prediction accuracy of sepsis for new patients without compromising accuracy for historical patients. On several electronic health record datasets, we will perform experiments to test the hypothesis that our methods will outperform state-of-the-art sepsis early prediction methods by more than 10%. Second, we will develop methods to automatically detect distribution shift from streaming sepsis data, improve the out-of-distribution (OOD) generalization capability of continual learning models, and quantify their prediction uncertainty. This will allow us to test the hypothesis that improving OOD generalization in continual learning will significantly improve the accuracy and robustness of early sepsis prediction on future patient cases.
