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GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records
[article]
2022
medRxiv
pre-print
There is an increasing interest in developing massive-size deep learning models in natural language processing (NLP) - the key technology to extract patient information from unstructured electronic health records (EHRs). However, there are limited studies exploring large language models in the clinical domain; the current largest clinical NLP model was trained with 110 million parameters (compared with 175 billion parameters in the general domain). It is not clear how large-size NLP models can
doi:10.1101/2022.02.27.22271257
fatcat:mqbbbce4wre5bbgdbkqd7o44fq