… However, recent advances in natural language processing (NLP) offer more efficient, …
Currently, about 80% of medical data are unstructured and do not fit into easily actionable …

… This study also utilized text classification which is a fundamental natural language processing
(… In the healthcare context, text classification is employed to make sense of a wide range of …

… study were characterized by: 1) natural language processing, 2) deep learning or neural
networks, and 3) clinical domain tasks using … These criteria were approximated through librarian-…

… The topic of clinical natural language processing and information extraction has been … ,
in particular with the introduction and adoption of electronic health records platforms. The …

… In this section, we present an overview of important concepts in deep learning and natural
language processing. In particular, we cover commonly used deep learning architectures in …

… for the research of clinical outcomes. Mental health research is one of the main focuses of
this study, … Introduction of the result of NLP with the phenotype makes it more difficult for the …

… This study examined whether Natural Language Processing (NLP) techniques could … of
responses thoroughly is more valuable than a cursory overview of a large number [36] . Another …

… a framework for considering natural language processing (NLP) approaches to public health
based on historical applications. This overview includes a brief introduction to AI and NLP, …

… Objective: Our objective was to offer natural language processing (NLP) experts a … Using
clinical Natural Language Processing for health outcomes research: Overview and actionable …

… , there has been a dramatic increase–now plateauing – in the use of e-cigarettes since their
introduction to developed world markets in around 2007 [86]. This increase has occurred in …