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Development of an Extractive Clinical Question Answering Dataset with Multi-Answer and Multi-Focus Questions
Specifically, the 1-to-N, M-to-1, and M-to-N drug-reason relations were included to form the multi-answer and multi-focus QA entries, which represent more complex and natural challenges in addition to the basic one-drug-one-reason cases.
Adapting and evaluating a deep learning language model for clinical why-question answering
Objectives: To adapt and evaluate a deep learning language model for answering why-questions based on patient-specific clinical text.
Clinical Concept Extraction: a Methodology Review
Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement.
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