[HTML][HTML] Scalable and accurate deep learning with electronic health records
Predictive modeling with electronic health record (EHR) data is anticipated to drive
personalized medicine and improve healthcare quality. Constructing predictive statistical …
personalized medicine and improve healthcare quality. Constructing predictive statistical …
Patient2vec: A personalized interpretable deep representation of the longitudinal electronic health record
The wide implementation of electronic health record (EHR) systems facilitates the collection
of large-scale health data from real clinical settings. Despite the significant increase in …
of large-scale health data from real clinical settings. Despite the significant increase in …
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 patient: an unsupervised representation to predict the future of patients from the electronic health records
Secondary use of electronic health records (EHRs) promises to advance clinical research
and better inform clinical decision making. Challenges in summarizing and representing …
and better inform clinical decision making. Challenges in summarizing and representing …
[HTML][HTML] Deep learning for electronic health records: A comparative review of multiple deep neural architectures
Despite the recent developments in deep learning models, their applications in clinical
decision-support systems have been very limited. Recent digitalisation of health records …
decision-support systems have been very limited. Recent digitalisation of health records …
The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review
Abstract Introduction: Electronic Health Record (EHR) is a significant source of medical data
that can be used to develop predictive modelling with therapeutically useful outcomes …
that can be used to develop predictive modelling with therapeutically useful outcomes …
Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis
The past decade has seen an explosion in the amount of digital information stored in
electronic health records (EHRs). While primarily designed for archiving patient information …
electronic health records (EHRs). While primarily designed for archiving patient information …
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
Early prediction of patient outcomes is important for targeting preventive care. This protocol
describes a practical workflow for developing deep-learning risk models that can predict …
describes a practical workflow for developing deep-learning risk models that can predict …
Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?
Abstract Machine learning can help clinicians to make individualized patient predictions only
if researchers demonstrate models that contribute novel insights, rather than learning the …
if researchers demonstrate models that contribute novel insights, rather than learning the …
Health-atm: A deep architecture for multifaceted patient health record representation and risk prediction
Leveraging massive electronic health records (EHR) brings tremendous promises to
advance clinical and precision medicine informatics research. However, it is very …
advance clinical and precision medicine informatics research. However, it is very …