A comprehensive study of named entity recognition in Chinese clinical text

Lei, Jianbo; Tang, Buzhou; Lu, Xueqin; Gao, Kaihua; Jiang, Min; Xu, Hua

doi:10.1136/amiajnl-2013-002381

Cited by 169 publications

(97 citation statements)

References 12 publications

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“…In particular, we choose a linear chain CRF; previous research shows that these perform well for various natural language processing tasks, especially Named Entity Recognition [Lei et al, 2014]. In contrast to general purpose noun phrase extractors used by some other existing models for this task, a CRF can easily exploit the information of the given annotated files as features.…”

Section: Methodsmentioning

confidence: 99%

Machine Reading for Extraction of Bacteria and Habitat Taxonomies

Kordjamshidi

Massa

Provoost

et al. 2015

Biomedical Engineering Systems and Technologies

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There is a vast amount of scientific literature available from various resources such as the internet. Automating the extraction of knowledge from these resources is very helpful for biologists to easily access this information. This paper presents a system to extract the bacteria and their habitats, as well as the relations between them. We investigate to what extent current techniques are suited for this task and test a variety of models in this regard. We detect entities in a biological text and map the habitats into a given taxonomy. Our model uses a linear chain Conditional Random Field (CRF). For the prediction of relations between the entities, a model based on logistic regression is built. Designing a system upon these techniques, we explore several improvements for both the generation and selection of good candidates. One contribution to this lies in the extended exibility of our ontology mapper that uses an advanced boundary detection and assigns the taxonomy elements to the detected habitats. Furthermore, we discover value in the combination of several distinct candidate generation rules. Using these techniques, we show results that are significantly improving upon the state of art for the BioNLP Bacteria Biotopes task.

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Section: Methodsmentioning

confidence: 99%

Machine Reading for Extraction of Bacteria and Habitat Taxonomies

Kordjamshidi

Massa

Provoost

et al. 2015

Biomedical Engineering Systems and Technologies

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“…Named entities are key fundamental elements in paragraphs and notes [5]. In clinical narratives, named entities are meaningful phrases such as diseases, symptoms, tests, medications, treatments and so on [6], which constitute the primary lines of the document and could be quickly parsed and reused to support writing assistant.…”

Section: Named Entity Recognitionmentioning

confidence: 99%

An Intelligent Writing Assistant Module for Narrative Clinical Records Based on Named Entity Recognition and Similarity Computation

Zhou¹,

Li²

2018

Proceedings of Information Science and Cloud Computing — PoS(ISCC 2017)

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Inpatient medical records which contain clinical narrative information generated from medical procedures have rich content and unlimited expression capabilities. In this paper, we proposed a novel method to assist health practitioners to write narrative clinical texts through a more efficient and safer manner. The core technologies supporting this work are named entity recognition (NER) and similarity computation: the CRF-based NER in this work has a good performance whose F-score has reached 89.23%, and the LDA model and similarity test has reached a precision of 71.28%. After these fundamental work, we designed and developed an intelligent writing assistant module: at sentence level, we used a conditional random field (CRF) method to train a NER model. When doctors type in an entity, several input candidates will pop up for selection; at paragraph level, we used a Gibbs-LDA++ tool and named entities to characterize the topics and key entities of existing records. When doctors create a new clinical text, the patient's structured data will be used as input to match similar paragraphs. As doctors keep typing in, the matching paragraphs also might change dynamically according to the input content. ISCC2017

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“…The early named entity recognition (NER) mostly uses rule-based methods, but they are highly dependent on language, field and text style of corpuses with limitation on cost effectiveness. Nowadays, NER researches mostly adopt statistical machine learning methods, such as the common use of HMM [1][2], SVM [3] [4], conditional random fields (CRF) [6] [7] etc., and more researches indicate that CRF is of favorable effect in NER work [1] [3].…”

Section: Introductionmentioning

confidence: 99%

Feature Split-based Information Extraction in the Field of Medicine

Wan¹,

Yan²,

Zhang³

2018

Proceedings of Information Science and Cloud Computing — PoS(ISCC 2017)

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In recent years, more and more studies have been done on symptom information extraction. These studies are mostly based on clinical medical records, and they focus only on symptom entities, which are not sufficient to convey the full symptom information. This paper presents a feature split-based approach to extract symptom information from Chinese medicine instruction texts. In this approach, the symptom information is split into two parts: symptom subject entity and symptom manifestation entity. The main idea of this method is to automatically recognize the symptom subject and symptom manifestation first, and then add these two identification results as features to the symptom information extraction task. Through a series of experiments based on Conditional Random Fields (CRF)-an effective model proved by lots of experiments in the field of medicine, it is obvious that the feature split-based approach proposed in this paper can obtain higher accuracy and recall rate in symptom information extraction. ISCC2017

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A comprehensive study of named entity recognition in Chinese clinical text

Cited by 169 publications

References 12 publications

Machine Reading for Extraction of Bacteria and Habitat Taxonomies

Machine Reading for Extraction of Bacteria and Habitat Taxonomies

An Intelligent Writing Assistant Module for Narrative Clinical Records Based on Named Entity Recognition and Similarity Computation

Feature Split-based Information Extraction in the Field of Medicine

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