GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records

TL;DR

GatorTron is a large-scale clinical language model trained on over 90 billion words, significantly improving performance on multiple clinical NLP tasks and demonstrating the benefits of scaling model size and training data in medical AI.

Contribution

This study introduces GatorTron, a large clinical language model with up to 8.9 billion parameters, trained on extensive clinical data, and systematically evaluates its impact on various NLP tasks.

Findings

Scaling up model size improves NLP task accuracy.

Training on extensive clinical data enhances model performance.

GatorTron outperforms smaller models on clinical NLP benchmarks.

Abstract

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model - GatorTron - using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on 5 clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve 5 clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og.