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 …
Modern clinical text mining: a guide and review
B Percha - Annual review of biomedical data science, 2021 - annualreviews.org
Electronic health records (EHRs) are becoming a vital source of data for healthcare quality
improvement, research, and operations. However, much of the most valuable information …
improvement, research, and operations. However, much of the most valuable information …
Clinical concept extraction with contextual word embedding
Automatic extraction of clinical concepts is an essential step for turning the unstructured data
within a clinical note into structured and actionable information. In this work, we propose a …
within a clinical note into structured and actionable information. In this work, we propose a …
A survey of named-entity recognition methods for food information extraction
As great amounts of food-related information is presented in the form of heterogeneous
textual data, computer-based methods are useful to automatically extract such information …
textual data, computer-based methods are useful to automatically extract such information …
A deep learning-based privacy-preserving model for smart healthcare in Internet of medical things using fog computing
With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big
data-driven medical applications have become even more important. The biomedical data …
data-driven medical applications have become even more important. The biomedical data …
An accurate deep learning model for clinical entity recognition from clinical notes
The growing use of electronic health records in the medical domain results in generating a
large amount of medical data that is stored in the form of clinical notes. These clinical notes …
large amount of medical data that is stored in the form of clinical notes. These clinical notes …
A deep language model for symptom extraction from clinical text and its application to extract COVID-19 symptoms from social media
Patients experience various symptoms when they haveeither acute or chronic diseases or
undergo some treatments for diseases. Symptoms are often indicators of the severity of the …
undergo some treatments for diseases. Symptoms are often indicators of the severity of the …
Character-level neural network model based on Nadam optimization and its application in clinical concept extraction
L Li, W Xu, H Yu - Neurocomputing, 2020 - Elsevier
Clinical concept extraction aims to quickly and effectively extract available data from
complex and diverse clinical information, which is a crucial task for medical diagnosis using …
complex and diverse clinical information, which is a crucial task for medical diagnosis using …
Information extraction from electronic medical records using multitask recurrent neural network with contextual word embedding
J Yang, Y Liu, M Qian, C Guan, X Yuan - Applied Sciences, 2019 - mdpi.com
Clinical named entity recognition is an essential task for humans to analyze large-scale
electronic medical records efficiently. Traditional rule-based solutions need considerable …
electronic medical records efficiently. Traditional rule-based solutions need considerable …
[PDF][PDF] The graph-based mutual attentive network for automatic diagnosis
Q Yuan, J Chen, C Lu, H Huang - Proceedings of the Twenty-Ninth …, 2021 - ijcai.org
The automatic diagnosis has been suffering from the problem of inadequate reliable corpus
to train a trustworthy predictive model. Besides, most of the previous deep learning based …
to train a trustworthy predictive model. Besides, most of the previous deep learning based …