A large language model for electronic health records

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five 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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives.
• 2 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).
• 3 It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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