Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …

Objectives Patient representation learning refers to learning a dense mathematical
representation of a patient that encodes meaningful information from Electronic Health …

Background The broad adoption of electronic health records (EHRs) provides great
opportunities to conduct health care research and solve various clinical problems in …

Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

Objective This systematic review aims to assess how information from unstructured text is
used to develop and validate clinical prognostic prediction models. We summarize the …

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 …

Causal explanations of the predictions of NLP systems are essential to ensure safety and
establish trust. Yet, existing methods often fall short of explaining model predictions …

Abstract Background Electronic health records (EHRs) provide possibilities to improve
patient care and facilitate clinical research. However, there are many challenges faced by …

We propose a deep learning-based multi-task learning (MTL) architecture focusing on
patient mortality predictions from clinical notes. The MTL framework enables the model to …