  We propose to meta-learn an a self-supervised patient trajectory forecast
learning rule by meta-training on a meta-objective that directly optimizes the
utility of the patient representation over the subsequent clinical outcome
prediction. This meta-objective directly targets the usefulness of a
representation generated from unlabeled clinical measurement forecast for later
supervised tasks.
  The meta-learned can then be directly used in target risk prediction, and the
limited available samples can be used for further fine-tuning the model
performance. The effectiveness of our approach is tested on a real open source
patient EHR dataset MIMIC-III. We are able to demonstrate that our
attention-based patient state representation approach can achieve much better
performance for predicting target risk with low resources comparing with both
direct supervised learning and pretraining with all-observation trajectory
forecast.
