Bidirectional Representation Learning from Transformers Using Multimodal Electronic Health Record Data to Predict Depression

Advancements in machine learning algorithms have had a beneficial impact on representation learning, classification, and prediction models built using electronic health record (EHR) data. Effort has been put both on increasing models' overall performance as well as improving their interpretabil...

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Bibliographic Details
Main Authors:	Arnold, C.W (Author), Meng, Y. (Author), Ong, M.K (Author), Speier, W. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	accuracy adult aged algorithm Algorithms area under the curve Area under the curves Article artificial neural network bidirectional representation learning breast cancer BRLTM model chronic disease Clinical decision support Clinical decision support systems Clustering algorithms controlled study Current Procedural Terminology Decision making Decision making process decision support system Decision support systems deep learning Deep learning depression Depression Diagnosis diagnostic test accuracy study Diseases eHealth E-learning electronic health record Electronic health record Electronic Health Records female fine tuning Forecasting heart infarction High dimensional data human Humans information retrieval Information Storage and Retrieval Interpretability learning algorithm Learning algorithms Learning systems liver cirrhosis machine learning Machine Learning major clinical study male masked language modeling natural language processing Patient Health Questionnaire 9 prediction Prediction model Predictive analytics pretraining receiver operating characteristic Records management sensitivity and specificity State of the art temporal deep learning temporal representation and reasoning training
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Bidirectional Representation Learning from Transformers Using Multimodal Electronic Health Record Data to Predict Depression

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