[HTML][HTML] 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 retrieval and text mining technologies for chemistry
Efficient access to chemical information contained in scientific literature, patents, technical
reports, or the web is a pressing need shared by researchers and patent attorneys from …
reports, or the web is a pressing need shared by researchers and patent attorneys from …
[HTML][HTML] The personal health applications of machine learning techniques in the internet of behaviors
Z Amiri, A Heidari, M Darbandi, Y Yazdani… - Sustainability, 2023 - mdpi.com
With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the
medical and healthcare fields are utilizing machine learning (ML) methodologies in …
medical and healthcare fields are utilizing machine learning (ML) methodologies in …
[HTML][HTML] Bidirectional RNN for medical event detection in electronic health records
AN Jagannatha, H Yu - Proceedings of the conference. Association …, 2016 - ncbi.nlm.nih.gov
Sequence labeling for extraction of medical events and their attributes from unstructured text
in Electronic Health Record (EHR) notes is a key step towards semantic understanding of …
in Electronic Health Record (EHR) notes is a key step towards semantic understanding of …
TaggerOne: joint named entity recognition and normalization with semi-Markov Models
Motivation: Text mining is increasingly used to manage the accelerating pace of the
biomedical literature. Many text mining applications depend on accurate named entity …
biomedical literature. Many text mining applications depend on accurate named entity …
[HTML][HTML] Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
In secondary analysis of electronic health records, a crucial task consists in correctly
identifying the patient cohort under investigation. In many cases, the most valuable and …
identifying the patient cohort under investigation. In many cases, the most valuable and …
[HTML][HTML] Bert-based ranking for biomedical entity normalization
Developing high-performance entity normalization algorithms that can alleviate the term
variation problem is of great interest to the biomedical community. Although deep learning …
variation problem is of great interest to the biomedical community. Although deep learning …
Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task
Manually curating chemicals, diseases and their relationships is significantly important to
biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical …
biomedical research, but it is plagued by its high cost and the rapid growth of the biomedical …
Development of phenotype algorithms using electronic medical records and incorporating natural language processing
Electronic medical records are emerging as a major source of data for clinical and
translational research studies, although phenotypes of interest need to be accurately …
translational research studies, although phenotypes of interest need to be accurately …
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 …