Meta Learning for Few-Shot Medical Text Classification

TL;DR

This paper explores meta-learning techniques for few-shot medical text classification, demonstrating improved data efficiency and robustness in medical note analysis compared to traditional methods.

Contribution

It introduces new data pipelines and extends meta-learning algorithms with DRO to enhance medical text classification performance and robustness.

Findings

Meta-learning improves data efficiency in medical text classification.

Meta-learning with DRO reduces worst-case loss across disease codes.

Meta-learning is effective for medical note data in few-shot settings.

Abstract

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.