|Title:||Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study||Authors:||YEN-PIN CHEN
|Keywords:||EHR; NLP; concept; deep learning; disease embedding; disease retrieval; electronic health record; emergency department; extraction; machine learning; natural language processing||Issue Date:||2021||Publisher:||JMIR PUBLICATIONS, INC||Journal Volume:||23||Journal Issue:||1||Source:||Journal of medical Internet research||Abstract:||
The electronic health record (EHR) contains a wealth of medical information. An organized EHR can greatly help doctors treat patients. In some cases, only limited patient information is collected to help doctors make treatment decisions. Because EHRs can serve as a reference for this limited information, doctors' treatment capabilities can be enhanced. Natural language processing and deep learning methods can help organize and translate EHR information into medical knowledge and experience.
adult; area under the curve; Article; clinical outcome; concept analysis; controlled study; critical care outcome; deep learning; deep neural network; diseases; electronic health record; embedding; emergency care; female; human; information retrieval; machine learning; major clinical study; male; medical information; natural language processing; receiver operating characteristic; university hospital; algorithm; electronic health record; information retrieval; natural language processing; procedures; Adult; Algorithms; Electronic Health Records; Female; Humans; Information Storage and Retrieval; Male; Natural Language Processing
|Appears in Collections:||醫學院附設醫院 (臺大醫院)|
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