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Natural language processing in electronic health records in relation to healthcare decision-making: a systematic review
Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
insights from Electronic Health Records (EHRs). However, the lack of annotated data …
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 …
Enhancing heart disease prediction using a self-attention-based transformer model
Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million
mortalities worldwide. The early detection of heart failure with high accuracy is crucial for …
mortalities worldwide. The early detection of heart failure with high accuracy is crucial for …
Protein–protein interaction site prediction through combining local and global features with deep neural networks
Abstract Motivation Protein–protein interactions (PPIs) play important roles in many
biological processes. Conventional biological experiments for identifying PPI sites are costly …
biological processes. Conventional biological experiments for identifying PPI sites are costly …
Automated machine learning for healthcare and clinical notes analysis
Machine learning (ML) has been slowly entering every aspect of our lives and its positive
impact has been astonishing. To accelerate embedding ML in more applications and …
impact has been astonishing. To accelerate embedding ML in more applications and …
[HTML][HTML] A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions
The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism
Motivation There is mounting evidence that the subcellular localization of lncRNAs can
provide valuable insights into their biological functions. In the real world of transcriptomes …
provide valuable insights into their biological functions. In the real world of transcriptomes …
DeepDISOBind: accurate prediction of RNA-, DNA-and protein-binding intrinsically disordered residues with deep multi-task learning
Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many
IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported …
IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported …
Clinical big data and deep learning: Applications, challenges, and future outlooks
The explosion of digital healthcare data has led to a surge of data-driven medical research
based on machine learning. In recent years, as a powerful technique for big data, deep …
based on machine learning. In recent years, as a powerful technique for big data, deep …
Recent advances in biomedical literature mining
The recent years have witnessed a rapid increase in the number of scientific articles in
biomedical domain. These literature are mostly available and readily accessible in …
biomedical domain. These literature are mostly available and readily accessible in …