A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization

Xu, Dongfang; Zhang, Zeyu; Bethard, Steven

doi:10.18653/v1/2020.acl-main.748

Cited by 31 publications

(9 citation statements)

References 35 publications

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“…Neural architectures have been widely used in recent state-of-the-art models for MCN from scientific texts, user reviews, social media texts and clinical notes ( Ji et al , 2020 ; Leaman and Lu, 2016 ; Li et al , 2017 , 2019 ; Miftahutdinov and Tutubalina, 2019 ; Sung et al , 2020 ; Xu et al , 2020 ; Zhao et al , 2019 ; Zhu et al , 2020 ). Most models share limitations regarding a supervised classification framework: (i) to retrieve concepts from a particular terminology for a given entity mention, models are required re-training, (ii) use additional classification or ranking layer, therefore, during inference compute all similarities between a given mention and all concept names from a dictionary through this layer and sort these scores in descending order.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, Ji et al (2020) fine-tuned BERT with binary classifier layer. Xu et al (2020) adopted a BERT-based multi-class classifier to generate a list of candidate concepts for each mention, and a BERT-based list-wise classifier to select the most likely candidate. We note that this multi-class candidate generator will require re-training for cross-terminology mapping.…”

Section: Related Workmentioning

confidence: 99%

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Medical concept normalization in clinical trials with drug and disease representation learning

Miftahutdinov¹,

Kadurin²,

Kudrin³

et al. 2021

Bioinformatics

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Motivation Clinical trials are the essential stage of every drug development program for the treatment to become available to patients. Despite the importance of well-structured clinical trial databases and their tremendous value for drug discovery and development such instances are very rare. Presently large-scale information on clinical trials is stored in clinical trial registers which are relatively structured, but the mappings to external databases of drugs and diseases are increasingly lacking. The precise production of such links would enable us to interrogate richer harmonized datasets for invaluable insights. Results We present a neural approach for medical concept normalization of diseases and drugs. Our two-stage approach is based on Bidirectional Encoder Representations from Transformers (BERT). In the training stage, we optimize the relative similarity of mentions and concept names from a terminology via triplet loss. In the inference stage, we obtain the closest concept name representation in a common embedding space to a given mention representation. We performed a set of experiments on a dataset of abstracts and a real-world dataset of trial records with interventions and conditions mapped to drug and disease terminologies. The latter includes mentions associated with one or more concepts (in-KB) or zero (out-of-KB, nil prediction). Experiments show that our approach significantly outperforms baseline and state-of-the-art architectures. Moreover, we demonstrate that our approach is effective in knowledge transfer from the scientific literature to clinical trial data. Availability We make code and data freely available at hidden\_during\_review\_process Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Medical concept normalization in clinical trials with drug and disease representation learning

Miftahutdinov¹,

Kadurin²,

Kudrin³

et al. 2021

Bioinformatics

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show abstract

“…These fine-tuned versions of BERT-based models are often combined with various machine learning approach to deliver good performance in biomedical normalization tasks. Ji et al [39] applied an ensemble approach based on Lucene and a pair-wise BERT classifier, and Xu et al [40] also proposed a hybrid system based on Lucene or a multi-class BERT classifier for the candidate generation, and a list-wise BERT classifier for ranking. BIOSYN [41] utilized entity representation from the BERT-based model and developed a synonym marginalization method with marginal maximum likelihood.…”

Section: Related Workmentioning

confidence: 99%

Re-Ranking System with BERT for Biomedical Concept Normalization

Cho

Choi

Lee³

2021

IEEE Access

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In recent years, various neural network architectures have been successfully applied to natural language processing (NLP) tasks such as named entity normalization. Named entity normalization is a fundamental task for extracting information in free text, which aims to map entity mentions in a text to gold standard entities in a given domain-specific ontology; however, the normalization task in the biomedical domain is still challenging because of multiple synonyms, various acronyms, and numerous lexical variations. In this study, we regard the task of biomedical entity normalization as a ranking problem, and propose an approach to rank normalized concepts. We additionally employ two factors that can notably affect the performance of normalization, such as task-specific pre-training (Task-PT) and calibration approach. Among five different biomedical benchmark corpora, our experimental results show that our proposed model achieved significant improvements over the previous methods and advanced the state-ofthe-art performance for biomedical entity normalization, with up to 0.5% increase in accuracy and 1.2% increase in F-score.

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“…[13] propose a multi-view convolutional neural network(CNN) and a multi-task framework to normalize both procedure and disease mentions. When the terminology knowledge base is large, [5,[15][16][17][18]21] propose a recall model to generate possible terminologies then followed by a rank model to sort. [16,17] first generate candidates by bm25, then rank the terminologies by CNN and Bert respectively.…”

Section: Medical Terminology Normalizationmentioning

confidence: 99%

A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization

Liang,

Xue,

Ruan

2021

Preprint

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Medical terminology normalization aims to map the clinical mention to terminologies come from a knowledge base, which plays an important role in analyzing Electronic Health Record(EHR) and many downstream tasks. In this paper, we focus on Chinese procedure terminology normalization. The expression of terminologies are various and one medical mention may be linked to multiple terminologies. Previous study explores some methods such as multi-class classification or learning to rank(LTR) to sort the terminologies by literature and semantic information. However, these information is inadequate to find the right terminologies, particularly in multi-implication cases. In this work, we propose a combined recall and rank framework to solve the above problems. This framework is composed of a multi-task candidate generator(MTCG), a keywords attentive ranker(KAR) and a fusion block(FB). MTCG is utilized to predict the mention implication number and recall candidates with semantic similarity. KAR is based on Bert with a keywords attentive mechanism which focuses on keywords such as procedure sites and procedure types. FB merges the similarity come from MTCG and KAR to sort the terminologies from different perspectives. Detailed experimental analysis shows our proposed framework has a remarkable improvement on both performance and efficiency.

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A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization

Cited by 31 publications

References 35 publications

Medical concept normalization in clinical trials with drug and disease representation learning

Medical concept normalization in clinical trials with drug and disease representation learning

Re-Ranking System with BERT for Biomedical Concept Normalization

A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization

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