  We present a novel data augmentation method to address the challenge of data
scarcity in modeling longitudinal patterns in Electronic Health Records (EHR)
of patients using natural language processing (NLP) algorithms. The proposed
method generates augmented data by rearranging the orders of medical records
within a visit where the order of elements are not obvious, if any. Applying
the proposed method to the clopidogrel treatment failure detection task enabled
up to 5.3% absolute improvement in terms of ROC-AUC (from 0.908 without
augmentation to 0.961 with augmentation) when it was used during the
pre-training procedure. It was also shown that the augmentation helped to
improve performance during fine-tuning procedures, especially when the amount
of labeled training data is limited.
