Continual learning of longitudinal health records
This addresses the incremental challenge of adapting continual learning to multi-variate sequential health data, which is crucial for clinical settings dealing with covariate shifts over time.
The paper tackled the problem of applying continual learning methods to longitudinal ICU data, finding that while several methods mitigate short-term forgetting, only replay-based methods achieved stable long-term performance in the face of domain shift.
Continual learning denotes machine learning methods which can adapt to new environments while retaining and reusing knowledge gained from past experiences. Such methods address two issues encountered by models in non-stationary environments: ungeneralisability to new data, and the catastrophic forgetting of previous knowledge when retrained. This is a pervasive problem in clinical settings where patient data exhibits covariate shift not only between populations, but also continuously over time. However, while continual learning methods have seen nascent success in the imaging domain, they have been little applied to the multi-variate sequential data characteristic of critical care patient recordings. Here we evaluate a variety of continual learning methods on longitudinal ICU data in a series of representative healthcare scenarios. We find that while several methods mitigate short-term forgetting, domain shift remains a challenging problem over large series of tasks, with only replay based methods achieving stable long-term performance. Code for reproducing all experiments can be found at https://github.com/iacobo/continual