The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text

BACKGROUND Named entity recognition (NER) plays an important role in extracting the features of descriptions for mining free-text radiology reports. However, the performance of existing NER tools is limited because the number of entities depends on its dictionary lookup. Especially, the recognition of compound terms is very complicated because there are a variety of patterns. OBJECTIVE The objective of the study is to develop and evaluate a NER tool concerned with compound terms using the RadLex for mining free-text radiology reports. METHODS We leveraged the clinical Text Analysis and Knowledge Extraction System (cTAKES) to develop customized pipelines using both RadLex and SentiWordNet (a general-purpose dictionary, GPD). We manually annotated 400 of radiology reports for compound terms (Cts) in noun phrases and used them as the gold standard for the performance evaluation (precision, recall, and F-measure). Additionally, we also created a compound-term-enhanced dictionary (CtED) by analyzing false negatives (FNs) and false positives (FPs), and applied it for another 100 radiology reports for validation. We also evaluated the stem terms of compound terms, through defining two measures: an occurrence ratio (OR) and a matching ratio (MR). RESULTS The F-measure of the cTAKES+RadLex+GPD was 32.2% (Precision 92.1%, Recall 19.6%) and that of combined the CtED was 67.1% (Precision 98.1%, Recall 51.0%). The OR indicated that stem terms of “effusion”, "node", "tube", and "disease" were used frequently, but it still lacks capturing Cts. The MR showed that 71.9% of stem terms matched with that of ontologies and RadLex improved about 22% of the MR from the cTAKES default dictionary. The OR and MR revealed that the characteristics of stem terms would have the potential to help generate synonymous phrases using ontologies. CONCLUSIONS We developed a RadLex-based customized pipeline for parsing radiology reports and demonstrated that CtED and stem term analysis has the potential to improve dictionary-based NER performance toward expanding vocabularies.

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A Study on the Impact of Intradomain Finetuning of Deep Language Models for Legal Named Entity Recognition in Portuguese

Intelligent Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-030-61377-8_46 ◽

2020 ◽

pp. 648-662

Author(s):

Luiz Henrique Bonifacio ◽

Paulo Arantes Vilela ◽

Gustavo Rocha Lobato ◽

Eraldo Rezende Fernandes

Keyword(s):

Named Entity Recognition ◽

Entity Recognition ◽

Language Models ◽

Named Entity ◽

The Impact

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Semi-Supervised Noisy Label Learning for Chinese Medical Named Entity Recognition

Data Intelligence ◽

10.1162/dint_a_00099 ◽

2021 ◽

pp. 1-10

Author(s):

Zhucong Li ◽

Zhen Gan ◽

Baoli Zhang ◽

Yubo Chen ◽

Jing Wan ◽

...

Keyword(s):

Hybrid System ◽

Named Entity Recognition ◽

Entity Recognition ◽

Final Test ◽

Named Entity ◽

The Core ◽

Category Labels ◽

Adversarial Training ◽

Core Idea ◽

The Impact

Abstract This paper describes our approach for the Chinese Medical named entity recognition(MER) task organized by the 2020 China conference on knowledge graph and semantic computing(CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record(EMR). We construct a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule postprocessing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we use post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.

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