  Medical professionals frequently work in a data constrained setting to
provide insights across a unique demographic. A few medical observations, for
instance, informs the diagnosis and treatment of a patient. This suggests a
unique setting for meta-learning, a method to learn models quickly on new
tasks, to provide insights unattainable by other methods. We investigate the
use of meta-learning and robustness techniques on a broad corpus of benchmark
text and medical data. To do this, we developed new data pipelines, combined
language models with meta-learning approaches, and extended existing
meta-learning algorithms to minimize worst case loss. We find that
meta-learning on text is a suitable framework for text-based data, providing
better data efficiency and comparable performance to few-shot language models
and can be successfully applied to medical note data. Furthermore,
meta-learning models coupled with DRO can improve worst case loss across
disease codes.
