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
Journal
Journal of medical Internet research
Journal Volume
23
Journal Issue
1
Date Issued
2021
Author(s)
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.
Subjects
EHR; NLP; concept; deep learning; disease embedding; disease retrieval; electronic health record; emergency department; extraction; machine learning; natural language processing
Other Subjects
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
Type
journal article