Named Entity Recognition in Electronic Health Records

A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its ...

Named Entity Recognition in Electronic Health Records - PubMed

EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support ...

Named entity recognition of Chinese electronic medical records ...

Named entity recognition (NER) of electronic medical records is an important task in clinical medical research.

Named Entity Recognition in Electronic Medical Records Based on ...

Abstract. Named entity recognition is the first step in clinical electronic medical record text mining, which is significant for clinical ...

A Comparison of Rule-based and Machine Learning Approaches

Abstract:This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data.

Named Entity Recognition Over Electronic Health Records Through ...

This paper proposes a combined approach for the recognition of named entities in such narrative texts. This approach is a composition of three different ...

(PDF) Named Entity Recognition in Electronic Health Records

Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text.

Named Entity Recognition in Healthcare - Fiive

NER is the process of identifying and categorizing key elements in text, such as names, dates, and other specific data.

Named Entity Recognition in Electronic Health Records Using ...

Abstract page for arXiv paper 1901.01592: Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural ...

Exploring named entity recognition and relation extraction for ...

This paper presents an approach to automatically extract information from these unstructured medical records using Domain Entity Recognition and Relation ...

BioBERT Based Named Entity Recognition in Electronic Medical ...

Abstract: The identification and mining of entities in Electronic Medical Record (EMR) plays an important role in medical diagnosis.

Medical Named Entity Recognition from Un-labelled Medical ...

Medical named entity recognition (NER) is an area in which medical named entities are recognized from medical texts, such as diseases, ...

Named entity recognition in electronic health records using transfer ...

Neural networks (NNs) have become the state of the art in many machine learning applications, such as image, sound (LeCun et al., 2015) and natural language ...

A Review on Electronic Health Record Text-Mining for Biomedical ...

Biomedical-named entity recognition (bNER) is critical in biomedical informatics. It identifies biomedical entities with special meanings, such as people, ...

Clinical entity classification in electronic health records | Snorkel AI

Recently, natural language processing (NLP) techniques such as named entity recognition (NER) have been used for automating tasks such as ...

[PDF] Named Entity Recognition for Electronic Health Records

This work investigates multiple approaches to Named Entity Recognition for text in Electronic Health Record (EHR) data and shows that a hand-crafted system ...

NER and Relation Extraction from Electronic Health Records (EHR).

First, the raw EHR document is preprocessed for a Named Entity Recognition model. This preprocessed data is then sent passed through either BiLSTM + CRF model ...

Integration of natural and deep artificial cognitive models in medical ...

These research findings and syntheses have not only provided scholars with a clearer understanding of NER in electronic medical records but also propelled the ...

Named Entity Recognition in Electronic Medical Records Based on ...

Named entity recognition is the first step in clinical electronic medical record text mining, which is significant for clinical decision support and ...

Clinical Named Entity Recognition From Chinese Electronic Health ...

Conclusions: In this study, we employed two computational methods to simultaneously identify types of Chinese clinical entities from free text ...