[HTML][HTML] Clinical text data in machine learning: systematic review
Background: Clinical narratives represent the main form of communication within health
care, providing a personalized account of patient history and assessments, and offering rich …
care, providing a personalized account of patient history and assessments, and offering rich …
Deep learning in clinical natural language processing: a methodical review
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 …
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …
Deep sentiment classification and topic discovery on novel coronavirus or COVID-19 online discussions: NLP using LSTM recurrent neural network approach
Internet forums and public social media, such as online healthcare forums, provide a
convenient channel for users (people/patients) concerned about health issues to discuss …
convenient channel for users (people/patients) concerned about health issues to discuss …
Enhancing clinical concept extraction with contextual embeddings
Objective Neural network–based representations (“embeddings”) have dramatically
advanced natural language processing (NLP) tasks, including clinical NLP tasks such as …
advanced natural language processing (NLP) tasks, including clinical NLP tasks such as …
[HTML][HTML] Chinese clinical named entity recognition with variant neural structures based on BERT methods
Abstract Clinical Named Entity Recognition (CNER) is a critical task which aims to identify
and classify clinical terms in electronic medical records. In recent years, deep neural …
and classify clinical terms in electronic medical records. In recent years, deep neural …
Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison
The biomedical literature is growing rapidly, and the extraction of meaningful information
from the large amount of literature is increasingly important. Biomedical named entity …
from the large amount of literature is increasingly important. Biomedical named entity …
Chinese clinical named entity recognition via multi-head self-attention based BiLSTM-CRF
Clinical named entity recognition (CNER) is a fundamental step for many clinical Natural
Language Processing (NLP) systems, which aims to recognize and classify clinical entities …
Language Processing (NLP) systems, which aims to recognize and classify clinical entities …
[HTML][HTML] Clinical concept extraction: a methodology review
Background Concept extraction, a subdomain of natural language processing (NLP) with a
focus on extracting concepts of interest, has been adopted to computationally extract clinical …
focus on extracting concepts of interest, has been adopted to computationally extract clinical …
Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations
Q Rafique, A Rehman, MS Afghan, HM Ahmad… - Computers in Biology …, 2023 - Elsevier
The COVID-19 pandemic has necessitated the development of reliable diagnostic methods
for accurately detecting the novel coronavirus and its variants. Deep learning (DL) …
for accurately detecting the novel coronavirus and its variants. Deep learning (DL) …
Machine learning techniques for biomedical natural language processing: a comprehensive review
EH Houssein, RE Mohamed, AA Ali - IEEE Access, 2021 - ieeexplore.ieee.org
The widespread use of electronic health records (EHR) systems in health care provides a
large amount of real-world data, leading to new areas for clinical research. Natural language …
large amount of real-world data, leading to new areas for clinical research. Natural language …