How can natural language processing help model informed drug development?: a review
Objective To summarize applications of natural language processing (NLP) in model
informed drug development (MIDD) and identify potential areas of improvement. Materials …
informed drug development (MIDD) and identify potential areas of improvement. Materials …
Applications of advanced natural language processing for clinical pharmacology
Natural language processing (NLP) is a branch of artificial intelligence, which combines
computational linguistics, machine learning, and deep learning models to process human …
computational linguistics, machine learning, and deep learning models to process human …
Overview of the first natural language processing challenge for extracting medication, indication, and adverse drug events from electronic health record notes (MADE …
Introduction This work describes the Medication and Adverse Drug Events from Electronic
Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 …
Health Records (MADE 1.0) corpus and provides an overview of the MADE 1.0 2018 …
A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora
Background Clinical trial protocols are the foundation for advancing medical sciences,
however, the extraction of accurate and meaningful information from the original clinical …
however, the extraction of accurate and meaningful information from the original clinical …
Detecting adverse drug events with rapidly trained classification models
Introduction Identifying occurrences of medication side effects and adverse drug events
(ADEs) is an important and challenging task because they are frequently only mentioned in …
(ADEs) is an important and challenging task because they are frequently only mentioned in …
AI-based language models powering drug discovery and development
Z Liu, RA Roberts, M Lal-Nag, X Chen, R Huang… - Drug Discovery …, 2021 - Elsevier
The discovery and development of new medicines is expensive, time-consuming, and often
inefficient, with many failures along the way. Powered by artificial intelligence (AI), language …
inefficient, with many failures along the way. Powered by artificial intelligence (AI), language …
[HTML][HTML] Using clinical natural language processing for health outcomes research: overview and actionable suggestions for future advances
The importance of incorporating Natural Language Processing (NLP) methods in clinical
informatics research has been increasingly recognized over the past years, and has led to …
informatics research has been increasingly recognized over the past years, and has led to …
Improving RNN with attention and embedding for adverse drug reactions
Electronic Health Records (EHR) narratives are a rich source of information, embedding
high-resolution information of value to secondary research use. However, because the …
high-resolution information of value to secondary research use. However, because the …
Review of natural language processing in pharmacology
D Trajanov, V Trajkovski, M Dimitrieva, J Dobreva… - Pharmacological …, 2023 - ASPET
Natural language processing (NLP) is an area of artificial intelligence that applies
information technologies to process the human language, understand it to a certain degree …
information technologies to process the human language, understand it to a certain degree …
[HTML][HTML] Annotated dataset creation through large language models for non-english medical NLP
J Frei, F Kramer - Journal of Biomedical Informatics, 2023 - Elsevier
Obtaining text datasets with semantic annotations is an effortful process, yet crucial for
supervised training in natural language processing (NLP). In general, developing and …
supervised training in natural language processing (NLP). In general, developing and …