This survey investigates current techniques for representing qualitative data for use as input
to neural networks. Techniques for using qualitative data in neural networks are well known …

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 …

A prominent technique for self-supervised representation learning has been to contrast
semantically similar and dissimilar pairs of samples. Without access to labels, dissimilar …

Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …

The utilization of large language models (LLMs) in the Healthcare domain has generated
both excitement and concern due to their ability to effectively respond to freetext queries with …

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 …

Motivation Graph embedding learning that aims to automatically learn low-dimensional
node representations, has drawn increasing attention in recent years. To date, most recent …

Objective: We explored whether use of deep learning to model temporal relations among
events in electronic health records (EHRs) would improve model performance in predicting …

Deep learning methods exhibit promising performance for predictive modeling in healthcare,
but two important challenges remain:-Data insufficiency: Often in healthcare predictive …

Leveraging large historical data in electronic health record (EHR), we developed Doctor AI,
a generic predictive model that covers observed medical conditions and medication uses …