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 …

Background Natural language processing applied to clinical text or aimed at a clinical
outcome has been thriving in recent years. This paper offers the first broad overview of …

Nowadays, voluminous unstructured data is steaming on the Web/social media. It is not easy
for individuals to find relevant information quickly from such a vast unstructured corpus. Text …

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 …

Background Traditional health information systems are generally devised to support clinical
data collection at the point of care. However, as the significance of the modern information …

Clinical information retrieval (IR) plays a vital role in modern healthcare by facilitating
efficient access and analysis of medical literature for clinicians and researchers. This …

The explosive growth and widespread accessibility of medical information on the Internet
have led to a surge of research activity in a wide range of scientific communities including …

Historical text archives constitute a rich and diverse source of information, which is
becoming increasingly readily accessible, due to large-scale digitisation efforts. However, it …

Hospitals represent complex organizations where a range of hospital workers, from
physicians to administrators, encounter a deluge of information they must quickly process …

Background There has been an increasing interest in learning low-dimensional vector
representations of medical concepts from Electronic Health Records (EHRs). Vector …