  Current deep learning based disease diagnosis systems usually fall short in
catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model
on new tasks usually leads to abrupt decay of performance on previous tasks.
What is worse, the trained diagnosis system would be fixed once deployed but
collecting training data that covers enough diseases is infeasible, which
inspires us to develop a lifelong learning diagnosis system. In this work, we
propose to adopt attention to combine medical entities and context, embedding
episodic memory and consolidation to retain knowledge, such that the learned
model is capable of adapting to sequential disease-diagnosis tasks. Moreover,
we establish a new benchmark, named Jarvis-40, which contains clinical notes
collected from various hospitals. Our experiments show that the proposed method
can achieve state-of-the-art performance on the proposed benchmark.
