The performance could not be improved by adding demographic data (age and sex). The human observers showed a better specificity (93%), while accuracy (84%) and sensitivity (76%) were similar to the ones of the algorithm. The authors concluded that the algorithm had difficulties identifying scaphoid fractures that are obvious to human observers.
MRE is to establish medical-related relations (e.g., induce relation) among clinical concepts (e.g., drugs, adverse events). MRE is usually approached as a classification problem—identify pairs of concepts with valid relations and classify the relation type. Various machine learning-based classifiers such as support vector machines (SVMs), random forests (RF), and gradient boosting trees (GBT)41 have been applied. With the emergence of deep learning models, researchers have explored the long-short-term memory (LSTM) architecture for RE in both general and clinical domains52,53. Most recently, several studies adopted the BERT architecture and demonstrated superior performance for MRE on various datasets54,55,56,57,58,59.
Readers also say the deck is great, of good quality, and compliments all their readings. Customers find the cards beautiful, with gorgeous silver edging. They say the booklet is detailed and thorough for professionals and beginners. We loaded the base model and the medium model into one GPU for distributed training.
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We conducted error analysis and compared GatorTron with ClinicalBERT to probe the observed performance improvements. We found that the larger, domain-specific pretrained models (e.g., GatorTron) are better at modeling longer phrases and determining semantic categories. For complex NLP tasks such as NLI and MQA, even large language models such as GatorTron still have difficulty in identifying the key pieces of information from longer paragraphs.
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