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Unlike other tasks focusing on phrases and sentences, MQA is a document-level task that requires information from the whole document to generate answers according to questions. In the general domain, the Stanford Question Answering Datasets (SQuAD 1.1 and 2.0)73,74 have been widely used as benchmarks. Transformer-based models are state-of-the-art for both SQuAD1.118 and SQuAD2.031. There are several MQA datasets developed in the past few years such as the MESHQA75, MedQuAD76, and emrQA77. We applied a span classification algorithm to identify the start and end offsets of the answer from the context. More specifically, we packed the question and the context into a single sequence as input for GatorTron and applied two linear layers to predict the start and end position of the answer, respectively.

By following the suggested framework for scoping reviews, we intended to give an informative overview and allow the reader to find a systematic approach to this very heterogenous topic. A systematic literature search of the MEDLINE/PubMed and Cochrane Collaboration libraries was conducted. The review was conducted according to the framework outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews. A narrative summary of the papers is presented to give an orienting overview of this rapidly evolving topic. In this new world of AI, our tasks will get automated, but people won’t; our jobs may change but they won’t get replaced.

Understanding the details of every language and environment and then translating them into appropriate formats can be such a difficult task for AI. GatorTron was pretrained using self-supervised masked language modeling (MLM) objective. We monitored training loss and calculated validation loss using a subset set of the clinical text (5%) to determine the appropriate stopping time. 2, we observed that larger GatorTron models converged faster than the smaller model.

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