Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …

The current industrial scenario has witnessed the application of several artificial intelligence-
based technologies for mining and processing IoMT-based big data. An emerging …

Here we represent human lives in a way that shares structural similarity to language, and we
exploit this similarity to adapt natural language processing techniques to examine the …

Irregular sampling occurs in many time series modeling applications where it presents a
significant challenge to standard deep learning models. This work is motivated by the …

The primary objective of implementing Electronic Health Records (EHRs) is to improve the
management of patients' health-related information. However, these records have also been …

Introduction Electronic health records contain an enormous amount of valuable information
recorded in free text. Information extraction is the strategy to transform free text into …

Several recent advancements in Machine Learning involve blackbox models: algorithms that
do not provide human-understandable explanations in support of their decisions. This …

Objective The aim of this study was to generate synthetic electronic health records (EHRs).
The generated EHR data will be more realistic than those generated using the existing …

Various deep learning models have recently been applied to predictive modeling of
Electronic Health Records (EHR). In medical claims data, which is a particular type of EHR …