Medical Named Entity Recognition using Surrounding Sequences Matching

Landolsi, Mohamed Yassine; Romdhane, Lotfi; Hlaoua, Lobna

doi:10.1016/j.procs.2022.09.122

Cited by 10 publications

(2 citation statements)

References 20 publications

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“…In contrast, pre-trained models used self-supervised learning methods to learn deep language representations from large-scale corpora, acquiring richer and more precise semantic features [15]. Additionally, pre-trained models possessed strong generalization and transfer learning capabilities, enabling them to accommodate diverse domains and tasks [16]. When faced with a small amount of labeled data, they achieved good performance with only fine-tuning.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Scale Local Attention Mechanism for Aspect Extraction

Yang,

Zhu,

et al. 2024

Preprint

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Aspect extraction is a critical step in constructing knowledge graphs and involves extracting aspect information from unstructured text. Current methods typically employ attention-based techniques such as global or local attention mechanisms, each with significant limitations. Global mechanisms are prone to introducing noise, while local mechanisms face challenges in determining the optimal window size. To address these issues, we propose a novel aspect extraction approach utilizing a multi-scale local attention mechanism (MLA). This method leverages a pre-trained model to convert text into vector representations. Feature extraction is then performed with gated recurrent units, followed by representation learning at various window sizes through the MLA. Features are selected using max pooling and decoded by a fully connected neural network combined with a conditional random field to generate precise aspect labels. Experimental validation on the Zhejiang Cup e-commerce review mining dataset demonstrates that our proposed method outperforms existing models in aspect extraction performance.

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Section: Related Workmentioning

confidence: 99%

A Multi-Scale Local Attention Mechanism for Aspect Extraction

Yang,

Zhu,

et al. 2024

Preprint

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“…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%

Named Entity Recognition in Electronic Health Records: A Methodological Review

Durango,

Torres-Silva,

Orozco-Duque

2023

Healthc Inform Res

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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.

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TD-CRESTS: Top-Down Chunk Retrieval Based on Entity, Section, and Topic Selection

Landolsi,

Ben Romdhane

2024

Lecture Notes in Business Information Processing

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Medical Named Entity Recognition using Surrounding Sequences Matching

Cited by 10 publications

References 20 publications

A Multi-Scale Local Attention Mechanism for Aspect Extraction

A Multi-Scale Local Attention Mechanism for Aspect Extraction

Named Entity Recognition in Electronic Health Records: A Methodological Review

TD-CRESTS: Top-Down Chunk Retrieval Based on Entity, Section, and Topic Selection

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