A survey on recent named entity recognition and relationship extraction techniques on clinical texts
Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …
Information extraction from electronic medical documents: state of the art and future research directions
MY Landolsi, L Hlaoua, L Ben Romdhane - Knowledge and Information …, 2023 - Springer
In the medical field, a doctor must have a comprehensive knowledge by reading and writing
narrative documents, and he is responsible for every decision he takes for patients …
narrative documents, and he is responsible for every decision he takes for patients …
2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records
Objective This article summarizes the preparation, organization, evaluation, and results of
Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on …
Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on …
[HTML][HTML] An empirical evaluation of prompting strategies for large language models in zero-shot clinical natural language processing: algorithm development and …
S Sivarajkumar, M Kelley… - JMIR Medical …, 2024 - medinform.jmir.org
Background Large language models (LLMs) have shown remarkable capabilities in natural
language processing (NLP), especially in domains where labeled data are scarce or …
language processing (NLP), especially in domains where labeled data are scarce or …
[HTML][HTML] Fine-tuning bidirectional encoder representations from transformers (BERT)–based models on large-scale electronic health record notes: an empirical study
Background: The bidirectional encoder representations from transformers (BERT) model has
achieved great success in many natural language processing (NLP) tasks, such as named …
achieved great success in many natural language processing (NLP) tasks, such as named …
Medical information extraction in the age of deep learning
Objectives: We survey recent developments in medical Information Extraction (IE) as
reported in the literature from the past three years. Our focus is on the fundamental …
reported in the literature from the past three years. Our focus is on the fundamental …
Adverse drug event detection using natural language processing: A scoping review of supervised learning methods
To reduce adverse drug events (ADEs), hospitals need a system to support them in
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …
Generative artificial intelligence GPT-4 accelerates knowledge mining and machine learning for synthetic biology
Knowledge mining from synthetic biology journal articles for machine learning (ML)
applications is a labor-intensive process. The development of natural language processing …
applications is a labor-intensive process. The development of natural language processing …
An overview of biomedical entity linking throughout the years
E French, BT McInnes - Journal of biomedical informatics, 2023 - Elsevier
Abstract Biomedical Entity Linking (BEL) is the task of mapping of spans of text within
biomedical documents to normalized, unique identifiers within an ontology. This is an …
biomedical documents to normalized, unique identifiers within an ontology. This is an …
Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods
Objective Identification of drugs, associated medication entities, and interactions among
them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events …
them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events …