Biomedical Named Entity Recognition Research Articles

Data augmentation and transfer learning for cross-lingual Named Entity Recognition in the biomedical domain

AbstractGiven the increase in production of data for the biomedical field and the unstoppable growth of the internet, the need for Information Extraction (IE) techniques has skyrocketed. Named Entity Recognition (NER) is one of such IE tasks useful for professionals in different areas. There are several settings where biomedical NER is needed, for instance, extraction and analysis of biomedical literature, relation extraction, organisation of biomedical documents, and knowledge-base completion. However, the computational treatment of entities in the biomedical domain has faced a number of challenges including its high cost of annotation, ambiguity, and lack of biomedical NER datasets in languages other than English. These difficulties have hampered data development, affecting both the domain itself and its multilingual coverage. The purpose of this study is to overcome the scarcity of biomedical data for NER in Spanish, for which only two datasets exist, by developing a robust bilingual NER model. Inspired by back-translation, this paper leverages the progress in Neural Machine Translation (NMT) to create a synthetic version of the Colorado Richly Annotated Full-Text (CRAFT) dataset in Spanish. Additionally, a new CRAFT dataset is constructed by replacing 20% of the entities in the original dataset generating a new augmented dataset. We evaluate two training methods: concatenation of datasets and continuous training to assess the transfer learning capabilities of transformers using the newly obtained datasets. The best performing NER system in the development set achieved an F-1 score of 86.39%. The novel methodology proposed in this paper presents the first bilingual NER system and it has the potential to improve applications across under-resourced languages.

RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition.

This article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework and to evaluate the improvement after applying RT framework. Given the remarkable advancements in retrieval-based language model and Chain-of-Thought across various natural language processing tasks, we propose a pioneering RT framework designed to amalgamate both approaches. The RT approach encompasses dedicated modules for information retrieval and Chain-of-Thought processes. In the retrieval module, RT discerns pertinent examples from demonstrations during instructional tuning for each input sentence. Subsequently, the Chain-of-Thought module employs a systematic reasoning process to identify entities. We conducted a comprehensive comparative analysis of our RT framework against 16 other models for few-shot NER tasks on BC5CDR and NCBI corpora. Additionally, we explored the impacts of negative samples, output formats, and missing data on performance. Our proposed RT framework outperforms other LMs for few-shot NER tasks with micro-F1 scores of 93.50 and 91.76 on BC5CDR and NCBI corpora, respectively. We found that using both positive and negative samples, Chain-of-Thought (vs Tree-of-Thought) performed better. Additionally, utilization of a partially annotated dataset has a marginal effect of the model performance. This is the first investigation to combine a retrieval-based LLM and Chain-of-Thought methodology to enhance the performance in biomedical few-shot NER. The retrieval-based LLM aids in retrieving the most relevant examples of the input sentence, offering crucial knowledge to predict the entity in the sentence. We also conducted a meticulous examination of our methodology, incorporating an ablation study. The RT framework with LLM has demonstrated state-of-the-art performance on few-shot NER tasks.

A Deep Learning Based Approach for Biomedical Named Entity Recognition Using Multitasking Transfer Learning with BiLSTM, BERT and CRF

A Deep Learning Based Approach for Biomedical Named Entity Recognition Using Multitasking Transfer Learning with BiLSTM, BERT and CRF

Language model based on deep learning network for biomedical named entity recognition

Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedical Named Entity Recognition and have shown encouraging results. However, many biological entities are polysemous and ambiguous, which is one of the main obstacles to the task of biomedical named entity recognition. Deep learning methods require large amounts of training data, so the lack of data also affect the performance of model recognition. To solve the problem of polysemous words and insufficient data, for the task of biomedical named entity recognition, we propose a multi-task learning framework fused with language model based on the BiLSTM-CRF architecture. Our model uses a language model to design a differential encoding of the context, which could obtain dynamic word vectors to distinguish words in different datasets. Moreover, we use a multi-task learning method to collectively share the dynamic word vector of different types of entities to improve the recognition performance of each type of entity. Experimental results show that our model reduces the false positives caused by polysemous words through differentiated coding, and improves the performance of each subtask by sharing information between different entity data. Compared with other state-of-the art methods, our model achieved superior results in four typical training sets, and achieved the best results in F1 values.

An effective undersampling method for biomedical named entity recognition using machine learning

An effective undersampling method for biomedical named entity recognition using machine learning

BioBBC: a multi-feature model that enhances the detection of biomedical entities

The rapid increase in biomedical publications necessitates efficient systems to automatically handle Biomedical Named Entity Recognition (BioNER) tasks in unstructured text. However, accurately detecting biomedical entities is quite challenging due to the complexity of their names and the frequent use of abbreviations. In this paper, we propose BioBBC, a deep learning (DL) model that utilizes multi-feature embeddings and is constructed based on the BERT-BiLSTM-CRF to address the BioNER task. BioBBC consists of three main layers; an embedding layer, a Long Short-Term Memory (Bi-LSTM) layer, and a Conditional Random Fields (CRF) layer. BioBBC takes sentences from the biomedical domain as input and identifies the biomedical entities mentioned within the text. The embedding layer generates enriched contextual representation vectors of the input by learning the text through four types of embeddings: part-of-speech tags (POS tags) embedding, char-level embedding, BERT embedding, and data-specific embedding. The BiLSTM layer produces additional syntactic and semantic feature representations. Finally, the CRF layer identifies the best possible tag sequence for the input sentence. Our model is well-constructed and well-optimized for detecting different types of biomedical entities. Based on experimental results, our model outperformed state-of-the-art (SOTA) models with significant improvements based on six benchmark BioNER datasets.

Advancing entity recognition in biomedicine via instruction tuning of large language models.

Large Language Models (LLMs) have the potential to revolutionize the field of Natural Language Processing, excelling not only in text generation and reasoning tasks but also in their ability for zero/few-shot learning, swiftly adapting to new tasks with minimal fine-tuning. LLMs have also demonstrated great promise in biomedical and healthcare applications. However, when it comes to Named Entity Recognition (NER), particularly within the biomedical domain, LLMs fall short of the effectiveness exhibited by fine-tuned domain-specific models. One key reason is that NER is typically conceptualized as a sequence labeling task, whereas LLMs are optimized for text generation and reasoning tasks. We developed an instruction-based learning paradigm that transforms biomedical NER from a sequence labeling task into a generation task. This paradigm is end-to-end and streamlines the training and evaluation process by automatically repurposing pre-existing biomedical NER datasets. We further developed BioNER-LLaMA using the proposed paradigm with LLaMA-7B as the foundational LLM. We conducted extensive testing on BioNER-LLaMA across three widely recognized biomedical NER datasets, consisting of entities related to diseases, chemicals, and genes. The results revealed that BioNER-LLaMA consistently achieved higher F1-scores ranging from 5% to 30% compared to the few-shot learning capabilities of GPT-4 on datasets with different biomedical entities. We show that a general-domain LLM can match the performance of rigorously fine-tuned PubMedBERT models and PMC-LLaMA, biomedical-specific language model. Our findings underscore the potential of our proposed paradigm in developing general-domain LLMs that can rival SOTA performances in multi-task, multi-domain scenarios in biomedical and health applications. Datasets and other resources are available at https://github.com/BIDS-Xu-Lab/BioNER-LLaMA.

Transformer-based Named Entity Recognition for Clinical Cancer Drug Toxicity by Positive-unlabeled Learning and KL Regularizers

Background: With increasing rates of polypharmacy, the vigilant surveillance of clinical drug toxicity has emerged as an important concern. Named Entity Recognition (NER) stands as an indispensable undertaking, essential for the extraction of valuable insights regarding drug safety from the biomedical literature. In recent years, significant advancements have been achieved in the deep learning models on NER tasks. Nonetheless, the effectiveness of these NER techniques relies on the availability of substantial volumes of annotated data, which is labor-intensive and inefficient. background: With increasing rates of polypharmacy, clinical drug toxicity has been closely monitored. Named Entity Recognition (NER) is a vital task for extracting valuable drug safety information from biomedical literature. Recently, many deep learning models in biomedical domain have made great progress for NER, especially pre-trained language models. However, these NER methods require large amounts of high-quality manually annotated data with named entities, which is labor intensive and inefficient. Methods: This study introduces a novel approach that diverges from the conventional reliance on manually annotated data. It employs a transformer-based technique known as Positive-Unlabeled Learning (PULearning), which incorporates adaptive learning and is applied to the clinical cancer drug toxicity corpus. To improve the precision of prediction, we employ relative position embeddings within the transformer encoder. Additionally, we formulate a composite loss function that integrates two Kullback-Leibler (KL) regularizers to align with PULearning assumptions. The outcomes demonstrate that our approach attains the targeted performance for NER tasks, solely relying on unlabeled data and named entity dictionaries. objective: To improve the performance of prediction Conclusion: Our model achieves an overall NER performance with an F1 of 0.819. Specifically, it attains F1 of 0.841, 0.801 and 0.815 for DRUG, CANCER, and TOXI entities, respectively. A comprehensive analysis of the results validates the effectiveness of our approach in comparison to existing PULearning methods on biomedical NER tasks. Additionally, a visualization of the associations among three identified entities is provided, offering a valuable reference for querying their interrelationships. method: In this work, instead of relying on the manually labeled data, a transformer-based Positive-Unlabeled Learning (PULearning) is proposed with adaptive learning and applied on the clinical cancer drug toxicity corpus. To improve the precision of prediction, relative position embeddings are used in transformer encoder. And then, a mixed loss is designed with two Kullback-Leibler (KL) regularizers for PULearning assumptions. Through adaptive sampling, our approach meets the expected performance for NER task only using unlabeled data and named entity dictionaries. result: The overall NER performance of our model obtains 0.819 of F1-score, while 0.841, 0.801 and 0.815 of F1-score on DRUG, CANCER and TOXI, respectively. other: None

RoBGP: A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

RoBGP: A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Dictionary-based matching graph network for biomedical named entity recognition

Biomedical named entity recognition (BioNER) is an essential task in biomedical information analysis. Recently, deep neural approaches have become widely utilized for BioNER. Biomedical dictionaries, implemented through a masked manner, are frequently employed in these methods to enhance entity recognition. However, their performance remains limited. In this work, we propose a dictionary-based matching graph network for BioNER. This approach utilizes the matching graph method to project all possible dictionary-based entity combinations in the text onto a directional graph. The network is implemented coherently with a bi-directional graph convolutional network (BiGCN) that incorporates the matching graph information. Our proposed approach fully leverages the dictionary-based matching graph instead of a simple masked manner. We have conducted numerous experiments on five typical Bio-NER datasets. The proposed model shows significant improvements in F1 score compared to the state-of-the-art (SOTA) models: 2.8% on BC2GM, 1.3% on BC4CHEMD, 1.1% on BC5CDR, 1.6% on NCBI-disease, and 0.5% on JNLPBA. The results show that our model, which is superior to other models, can effectively recognize natural biomedical named entities.

A BIGRU-Based Stacked Attention Network for Biomedical Named Entity Recognition with Chinese EMRs.

Biomedical named entity recognition (BNER) is an effective method to structure the medical text data. It is an important basic task for building the medical application services such as the medical knowledge graphs and the intelligent auxiliary diagnosis systems. Existing medical named entity recognition methods generally leverage the word embedding model to construct text representation, and then integrate multiple semantic understanding models to enhance the semantic understanding ability of the model to achieve high-performance entity recognition. However, in the medical field, there are many professional terms that rarely appear in the general field, which cannot be represented well by the general domain word embedding model. Second, existing approaches typically only focus on the extraction of global semantic features, which generate a loss of local semantic features between characters. Moreover, as the word embedding dimension becomes much higher, the standard single-layer structure fails to fully and deeply extract the global semantic features. We put forward the BIGRU-based Stacked Attention Network (BSAN) model for biomedical named entity recognition. Firstly, we use the large-scale real-world medical electronic medical record (EMR) data to fine-tune BERT to build the proprietary embedding representations of the medical terms. Second, we use the Convolutional Neural Network model to extract semantic features. Finally, a stacked BIGRU is constructed using a multi-layer structure and a novel stacking method. It not only enables comprehensive and in-depth extraction of global semantic features, but also requires less time. Experimentally validated on the real-world datasets in Chinese EMRs, the proposed BSAN model achieves 90.9% performance on F1-values, which is stronger than the BNER performance of other state-of-the-art models.

A dictionary-guided attention network for biomedical named entity recognition in Chinese electronic medical records

A dictionary-guided attention network for biomedical named entity recognition in Chinese electronic medical records

MMBERT: a unified framework for biomedical named entity recognition.

Named entity recognition (NER) is an important task in natural language processing (NLP). In recent years, NER has attracted much attention in the biomedical field. However, due to the lack of biomedical named entity identification datasets, the complexity and rarity of biomedical named entities and so on, biomedical NER is more difficult than general domain NER. So in this paper, we propose a framework (MMBERT) based on Transformer to solve the problems above. To address the scarcity of biomedical named entity recognition datasets, we introduce ERNIE-Health, a new Chinese language representation model pre-trained on large-scale biomedical text corpora. Because of the complexity and rarity of biomedical named entities, we use the Bert and CW-LSTM structures to get the joint feature vector of word pairs relations. In addition, we design multi-granularity 2D convolution to refine the relationship and representation between word pairs. Finally, we design a convolutional neural network (CNN) structure and a co-predictor to improve the model's generalization capability and prediction accuracy. We have conducted extensive experiments on three benchmark datasets, and the experimental results show that our model achieves the best results compared with several baseline models in the experiment.

Data augmentation via context similarity: An application to biomedical Named Entity Recognition

In this paper, we present COntext SImilarity-based data augmentation for NER (COSINER), a new method for improving Named Entity Recognition (NER) tasks using data augmentation. Unlike current techniques, which may generate noisy and mislabeled samples through text manipulation, COSINER uses context similarity to replace entity mentions with more plausible ones on the basis of available training data and considering the context in which entities typically appear. Through experiments on popular benchmark datasets, we show that COSINER outperforms existing baselines in various few-shot scenarios where training data is limited. Additionally, our method’s computing times are comparable to the simplest augmentation methods and are better than approaches that rely on pre-trained models in their architecture.

Biomedical named entity recognition using TCN approaches and bio tagging

<p>Biomedical named entity recognition (BNER) is to identify instances in biomedical field such as chemical compounds, drugs, genes, RNA, DNA and proteins used in extracting information. It extracts relation between various drugs and their usage, profiles of similar and related drugs with help of machine learning approach. The efficiency in biomedical field is still in research for further improvement even many supervised methods are applied. The proposed method combines two algorithms and improve performance based on features used. It uses conditional random field (CRF) for entity identification and classification of temporal conventional network (TCN) to detect and recognize subtypes in BNER. Datasets such as GENIA and CHEMDNER corpus are used for evaluation with different entity types. Results shows that proposed methods performed better compared to other machine learning approach. The detailed study of TCN has been discussed. The classification of BNER is mapped with various classification methods to enhance result of high recognition.</p>

An Effective Biomedical Named Entity Recognition by Handling Imbalanced Data Sets Using Deep Learning and Rule-Based Methods

An Effective Biomedical Named Entity Recognition by Handling Imbalanced Data Sets Using Deep Learning and Rule-Based Methods

Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning.

Few-shot learning (FSL) that can effectively perform named entity recognition in low-resource scenarios has raised growing attention, but it has not been widely studied yet in the biomedical field. In contrast to high-resource domains, biomedical named entity recognition (BioNER) often encounters limited human-labeled data in real-world scenarios, leading to poor generalization performance when training only a few labeled instances. Recent approaches either leverage cross-domain high-resource data or fine-tune the pre-trained masked language model using limited labeled samples to generate new synthetic data, which is easily stuck in domain shift problems or yields low-quality synthetic data. Therefore, in this paper, we study a more realistic scenario, i.e., few-shot learning for BioNER. Leveraging the domain knowledge graph, we propose knowledge-guided instance generation for few-shot BioNER, which generates diverse and novel entities based on similar semantic relations of neighbor nodes. In addition, by introducing question prompt, we cast BioNER as question answering (QA) task and propose prompt contrastive learning to improve the robustness of the model by measuring the mutual information (MI) between query-answer pairs. Extensive experiments conducted on various few-shot settings show that the proposed framework achieves superior performance. Particularly, in a low-resource scenario with only 20 samples, our approach substantially outperforms recent state-of-the-art (SoTA) models on four benchmark datasets, achieving an average improvement of up to 7.1% F1. Our source code and data are available at https://github.com/cpmss521/KGPC. Supplementary data are available at Bioinformatics online.

Biomedical-named entity recognition using CUDA accelerated KNN algorithm

Biomedical named entity recognition (Bio-NER) is a highly complex and time-consuming research domain using natural language processing (NLP). It’s widely used in information retrieval, knowledge summarization, biomolecular event extraction, and discovery applications. This paper proposes a method for the recognition and classification of named entities in the biomedical domain using machine learning (ML) techniques. Support vector machine (SVM), decision trees (DT), K-nearest neighbor (KNN), and its kernel versions are used. However, recent advancements in programmable, massively parallel graphics processing units (GPU) hold promise in terms of increased computational capacity at a lower cost to address multi-dimensional data and time complexity. We implement a novel parallel version of KNN by porting the distance computation step on GPU using the compute unified device architecture (CUDA) and compare the performance of all the algorithms using the BioNLP/NLPBA 2004 corpus. Results demonstrate that CUDA-KNN takes full advantage of the GPU’s computational capacity and multi-leveled memory architecture, resulting in a 35× performance enhancement over the central processing unit (CPU). In a comparative study with existing research, the proposed model provides an option for a faster NER system for higher dimensionality and larger datasets as it offers balanced performance in terms of accuracy and speed-up, thus providing critical design insights into developing a robust BioNLP system.

Transferring from Textual Entailment to Biomedical Named Entity Recognition.

Biomedical Named Entity Recognition (BioNER) aims at identifying biomedical entities such as genes, proteins, diseases, and chemical compounds in the given textual data. However, due to the issues of ethics, privacy, and high specialization of biomedical data, BioNER suffers from the more severe problem of lacking in quality labeled data than the general domain especially for the token-level. Facing the extremely limited labeled biomedical data, this work studies the problem of gazetteer-based BioNER, which aims at building a BioNER system from scratch. It needs to identify the entities in the given sentences when we have zero token-level annotations for training. Previous works usually use sequential labeling models to solve the NER or BioNER task and obtain weakly labeled data from gazetteers when we don't have full annotations. However, these labeled data are quite noisy since we need the labels for each token and the entity coverage of the gazetteers is limited. Here we propose to formulate the BioNER task as a Textual Entailment problem and solve the task via Textual Entailment with Dynamic Contrastive learning (TEDC). TEDC not only alleviates the noisy labeling issue, but also transfers the knowledge from pre-trained textual entailment models. Additionally, the dynamic contrastive learning framework contrasts the entities and non-entities in the same sentence and improves the model's discrimination ability. Experiments on two real-world biomedical datasets show that TEDC can achieve state-of-the-art performance for gazetteer-based BioNER.

Provision and Characterization of a Corpus for Pharmaceutical, Biomedical Named Entity Recognition for Pharmacovigilance: Evaluation of Language Registers and Training Data Sufficiency.

Machine learning (ML) systems are widely used for automatic entity recognition in pharmacovigilance. Publicly available datasets do not allow the use of annotated entities independently, focusing on small entity subsets or on single language registers (informal or scientific language). The objective of the current study was to create a dataset that enables independent usage of entities, explores the performance of predictive ML models on different registers, and introduces a method to investigate entity cut-off performance. A dataset has been created combining different registers with 18 different entities. We applied this dataset to compare the performance of integrated models with models created with single language registers only. We introduced fractional stratified k-fold cross-validation to determine model performance on entity level by using training dataset fractions. We investigated the course of entity performance with fractions of training datasets and evaluated entity peak and cut-off performance. The dataset combines 1400 records (scientific language: 790; informal language: 610) with 2622 sentences and 9989 entity occurrences and combines data from external (801 records) and internal sources (599 records). We demonstrated that single language register models underperform compared to integrated models trained with multiple language registers. A manually annotated dataset with a variety of different pharmaceutical and biomedical entities was created and is made available to the research community. Our results show that models that combine different registers provide better maintainability, have higher robustness, and have similar or higher performance. Fractional stratified k-fold cross-validation allows the evaluation of training data sufficiency on the entity level.