Extraction of risk factors for cardiovascular diseases from Chinese electronic medical records

Su, Jia; Hu, Jinpeng; Jiang, Junqiu; Xie, Jing; Yang, Yang; He, Bin; Yang, Jinfeng; Guan, Yi

doi:10.1016/j.cmpb.2019.01.007

Cited by 18 publications

(7 citation statements)

References 28 publications

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“…Most of the selected publications involved corpora written in English: 94 of the 105 reviewed articles. Publications in languages other than English include [39][40][41][42] in Chinese, [43,44] in Korean, [45] in Dutch, [46] in Italian, [47] in Swedish, and [48] in Spanish. Additionally, [31] dealt with English and French, extracting temporal relations from both the THYME corpus and the MERLOT corpus [49], which are from medical texts in French.…”

Section: Global Quantitative Resultsmentioning

confidence: 99%

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Temporal Relation Extraction in Clinical Texts

et al. 2021

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Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning–based models with few recent initiatives to push the state-of-the-art with deep learning–based models. For temporal relations between entities (event and temporal expressions) in the document, factors such as dataset imbalance because of candidate pair generation and task complexity directly affect the system's performance. The state-of-the-art resides on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. However, further experiments and advances in the research topic are required until real-time clinical domain applications are released. Furthermore, most of the publications mainly reside on the same dataset, hindering the need for new annotation projects that provide datasets for different medical specialties, clinical text types, and even languages.

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Section: Global Quantitative Resultsmentioning

confidence: 99%

“…However, the results were still not comparable with those of traditional machine learning algorithms. For regular datasets, the authors of [40] extracted risk factors for cardiovascular diseases, similar to the i2b2/UTHealth 2014 shared-task objective, using a CNN-based model.…”

Section: Authorsmentioning

confidence: 99%

Temporal Relation Extraction in Clinical Texts

et al. 2021

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“…In addition, in the process of annotation of the military text corpus, the principle of "no overlapping, no nesting, and no pause punctuation" that is similar to that for literature should be followed. Since nonstandard Chinese word segmentation may cause error transmission problems for subsequent named entity recognition tasks, a BIO threesegment annotation method is used in the sentence-bysentence annotation process of military corpus [28], in which the starting word for each entity is marked as "Bentity type," the subsequent mark is "I-entity type," and O means nonentity part. erefore, there are a total of 31 types of tags in the labeled corpus, as shown in Table 1.…”

Section: Entity Annotation System and Methodmentioning

confidence: 99%

Research on Application of Intelligent Corpus Annotation of Entity Extraction with Construction of Knowledge Graph

Liu

Fan

2022

Mathematical Problems in Engineering

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The purpose of this paper is to solve the problem of big data and small samples caused by the high manual annotation cost of a military corpus. The deep learning algorithm of entity extraction in the military field was organically combined with the method of bootstrapping loop iteration to complete a study on the application of intelligent corpus annotation of military field entities. With the experimental research showing that using a small number of military field entity corpus annotations for RoBERTa pretraining word vectors and BiLSTM-CRF models and based on the bootstrapping algorithm idea to complete 3 rounds of loop iterations and 10 rounds of cross-validation joint-voting model iterations, the best entity extraction model evaluation F value reached up to 91.5%. Finally, the 60M intelligent corpus annotation application testing was completed using the best model of iteration of this round, with a total of 178,177 sentences of military field corpus intelligently labeled, the number of entities that should be labeled reaching 417,734. Therefore, this is an efficient way of construction and evaluation of intelligent corpus annotation model in the military entity extraction field. The findings of this paper provide an effective way of how to complete the labeled corpus. The research serves as a first step for future research, for example, the construction of knowledge graphs and military intelligent Q&A.

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“…The first papers to report on NER models, based on deep learning and applied to EHRs, were published in 2015 [27]. By 2019, bidirectional long short-term memory (BiLSTM) had become the dominant architecture [7,[28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. That same year, three studies were published that utilized the bidirectional encoder representations from transformers (BERT) architecture [28,43,46].…”

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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Extraction of risk factors for cardiovascular diseases from Chinese electronic medical records

Cited by 18 publications

References 28 publications

Temporal Relation Extraction in Clinical Texts

Temporal Relation Extraction in Clinical Texts

Research on Application of Intelligent Corpus Annotation of Entity Extraction with Construction of Knowledge Graph

Named Entity Recognition in Electronic Health Records: A Methodological Review

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