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Right, No Matter Why: AI Fact-checking and AI Authority in Health-related Inquiry Settings
Previous research on expert advice-taking shows that humans exhibit two contradictory behaviors: on the one hand, people tend to overvalue their own opinions undervaluing the expert opinion, and on the other, people often defer to other people's advice even if the advice itself is rather obviously wrong.
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
In this review, we focus on the interpretability of the DL models in healthcare.
Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text
Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there has been numerous reports on improved performance on a variety of natural language tasks.
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network
SemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types.
CliNER 2.0: Accessible and Accurate Clinical Concept Extraction
Clinical notes often describe important aspects of a patient's stay and are therefore critical to medical research.
