Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review
Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
record (EHR) data, and illustrate various deep learning architectures for analyzing different …
[HTML][HTML] Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review
Objectives Patient representation learning refers to learning a dense mathematical
representation of a patient that encodes meaningful information from Electronic Health …
representation of a patient that encodes meaningful information from Electronic Health …
Mining electronic health records (EHRs) A survey
The continuously increasing cost of the US healthcare system has received significant
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …
Boosting deep learning risk prediction with generative adversarial networks for electronic health records
The rapid growth of Electronic Health Records (EHRs), as well as the accompanied
opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests …
opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests …
Learning for personalized medicine: a comprehensive review from a deep learning perspective
With the recent advancements in analyzing high-volume, complex, and unstructured data,
modern learning methods are playing an increasingly critical role in the field of personalized …
modern learning methods are playing an increasingly critical role in the field of personalized …
Risk prediction on electronic health records with prior medical knowledge
Predicting the risk of potential diseases from Electronic Health Records (EHR) has attracted
considerable attention in recent years, especially with the development of deep learning …
considerable attention in recent years, especially with the development of deep learning …
Measuring patient similarities via a deep architecture with medical concept embedding
Evaluating the clinical similarities between pairwisepatients is a fundamental problem in
healthcare informatics. Aproper patient similarity measure enables various …
healthcare informatics. Aproper patient similarity measure enables various …
The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures
J Chen, D Chun, M Patel, E Chiang, J James - BMC medical informatics …, 2019 - Springer
Background Clinical data synthesis aims at generating realistic data for healthcare research,
system implementation and training. It protects patient confidentiality, deepens our …
system implementation and training. It protects patient confidentiality, deepens our …
[HTML][HTML] SECNLP: A survey of embeddings in clinical natural language processing
KS Kalyan, S Sangeetha - Journal of biomedical informatics, 2020 - Elsevier
Distributed vector representations or embeddings map variable length text to dense fixed
length vectors as well as capture prior knowledge which can transferred to downstream …
length vectors as well as capture prior knowledge which can transferred to downstream …
Early diagnosis and prediction of sepsis shock by combining static and dynamic information using convolutional-LSTM
Deep neural network models, especially Long Short Term Memory (LSTM), have shown
great success in analyzing Electronic Health Records (EHRs) due to their ability to capture …
great success in analyzing Electronic Health Records (EHRs) due to their ability to capture …