Development and validation of deep continual learning model to sequentially learn multiple clinical prediction tasks for ICU patients.

BACKGROUND: ICU patients often suffer from critical and complex condition, and multiple potential risks should be monitored to provide them comprehensive care. However, no study proposes continual learning (CL) model that can effectively solve multiple clinical prediction tasks without catastrophic forgetting. This study proposes three deep CL models for ICU patients. METHODS: Three public ICU databases were employed. The included patients from MIMIC-III and MIMIC-IV were divided into eight task sets, and the patients from eICU-CRD composed the test set. We propose three CL models (CL_1, CL_2, CL_3) to sequentially learn eight prediction tasks on the eight task sets, and then externally validate them on the test set. We compare our models to three representative baseline CL models and the single-task (ST) and multi-task (MT) model. We train all the CL models under different orders, and evaluate their prediction performance by multiple metrics and their memory ability by backward transfer (BWT). We also analyzed the effect of previously learned tasks on learning new tasks. RESULTS: Our three CL models had comparable or slightly weaker performance compared to ST and MT model on the eight tasks. They effectively mitigated catastrophic forgetting, and their performance is robust to different training orders. CL_2 and CL_3 even have improved performance on the current task after learning some previous tasks. Our three CL models outperformed the baseline CL models in most experiments. CONCLUSIONS: Our CL models are promising to sequentially learn multiple clinical prediction tasks for ICU patients. The CL_2 and CL_3 show the ability of utilizing information of previous tasks to improve learning new tasks. More new datasets and tasks are still needed to further verify the validity of the CL models.