Surgical procedure long terms recognition from Chinese literature incorporating structural feature

Nan, Jiale; Gao, Dongping; Sun, Yuanyuan; Li, Xiaoying; Shen, Xifeng; Li, Meiting; Zhang, Weining; Ren, Huiling; Qu, Yi

doi:10.1016/j.heliyon.2022.e11291

Cited by 1 publication

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the CRF model was commonly used in hybrid models or as an additional layer in the output of other models. Specifically, CRF was integrated with rule-based approaches [9,16,[19][20][21][22][23], deep learning methods [4,24,25], and the conditional Markov model [26].…”

Section: Classification Modelsmentioning

confidence: 99%

“…That same year, three studies were published that utilized the bidirectional encoder representations from transformers (BERT) architecture [28,43,46]. By 2021 [47][48][49][50][51][52][53][54], the BERT architecture and its variants had emerged as the primary NER model applied to EHRs, a trend that continues to this day [4,24,25,[55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71]. However, this self-attention mechanism was initially introduced in 2017 [72].…”

Section: Classification Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Named Entity Recognition in Electronic Health Records: A Methodological Review

Durango,

Torres-Silva,

Orozco-Duque

2023

Healthc Inform Res

View full text Add to dashboard Cite

Objectives: A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022.Methods: We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora.Results: Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain.Conclusions: EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice.

show abstract