  Deep learning models have demonstrated superior performance in various
healthcare applications. However, the major limitation of these deep models is
usually the lack of high-quality training data due to the private and sensitive
nature of this field. In this study, we propose a novel textual data
augmentation method to generate artificial clinical notes in patients'
Electronic Health Records (EHRs) that can be used as additional training data
for patient outcomes prediction. Essentially, we fine-tune the generative
language model GPT-2 to synthesize labeled text with the original training
data. More specifically, We propose a teacher-student framework where we first
pre-train a teacher model on the original data, and then train a student model
on the GPT-augmented data under the guidance of the teacher. We evaluate our
method on the most common patient outcome, i.e., the 30-day readmission rate.
The experimental results show that deep models can improve their predictive
performance with the augmented data, indicating the effectiveness of the
proposed architecture.
