Lifelong Learning based Disease Diagnosis on Clinical Notes
This addresses the problem of performance decay in deployed medical diagnosis systems for healthcare, though it is incremental as it builds on existing lifelong learning techniques.
The paper tackles catastrophic forgetting in deep learning disease diagnosis systems by developing a lifelong learning approach that uses attention, episodic memory, and consolidation to adapt to sequential tasks, achieving state-of-the-art performance on the new Jarvis-40 benchmark.
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.