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Comparison of active learning algorithms in classifying head computed tomography reports using bidirectional encoder representations from transformers

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2025年第4期20卷 687-701页

作者： Wataya, Tomohiro Miura, Azusa Sakisuka, Takahisa Fujiwara, Masahiro Tanaka, Hisashi Hiraoka, Yu Sato, Junya Tomiyama, Miyuki Nishigaki, Daiki Kita, Kosuke Suzuki, Yuki Kido, Shoji Tomiyama, Noriyuki Osaka Univ Dept Radiol Grad Sch Med 2-2Yamadaoka Suita Osaka 5650871 Japan Osaka Univ Dept Artificial Intelligence Diagnost Radiol Grad Sch Med 2-2Yamadaoka Suita Osaka 5650871 Japan Osaka Gen Med Ctr Dept Diagnost Imaging 3-1-56 Mandai HigashiSumiyoshi Osaka 5588558 Japan Sakai City Med Ctr Dept Diagnost Radiol 1-1-1Ebaracho Sakai Osaka 5938304 Japan Osaka Univ Div Hlth Sci Grad Sch Med 1-7 Yamadaoka Suita Osaka 5650871 Japan

Purpose: Systems equipped with natural language (NLP) processing can reduce missed radiological findings by physicians, but the annotation costs are burden in the development. This study aimed to compare the effects of active learning (AL) algorithms in NLP for estimating the significance of head computed tomography (CT) reports using bidirectional encoder representations from transformers (BERT). Methods: A total of 3728 head CT reports annotated with five categories of importance were used and UTH-BERT was adopted as the pre-trained BERT model. We assumed that 64% (2385 reports) of the data were initially in the unlabeled data pool (UDP), while the labeled data set (LD) used to train the model was empty. Twenty-five reports were repeatedly selected from the UDP and added to the LD, based on seven metrices: random sampling (RS: control), four uncertainty sampling (US) methods (least confidence (LC), margin sampling (MS), ratio of confidence (RC), and entropy sampling (ES)), and two distance-based sampling (DS) methods (cosine distance (CD) and Euclidian distance (ED)). The transition of accuracy of the model was evaluated using the test dataset. Results: The accuracy of the models with US was significantly higher than RS when reports in LD were < 1800, whereas DS methods were significantly lower than RS. Among the US methods, MS and RC were even better than the others. With the US methods, the required labeled data decreased by 15.4-40.5%, and most efficient in RC. In addition, in the US methods, data for minor categories tended to be added to LD earlier than RS and DS. Conclusions: In the classification task for the importance of head CT reports, US methods, especially RC and MS can lead to the effective fine-tuning of BERT models and reduce the imbalance of categories. AL can contribute to other studies on larger datasets by providing effective annotation.

关键词： Natural language processing Active learning bidirectional encoder representations from transformers Radiology report Medical safety

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LDAViewer: An Automatic Language-Agnostic System for Discovering State-of-the-Art Topics in Research Using Topic Modeling, bidirectional encoder representations from transformers, and Entity Linking

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IEEE ACCESS 2023年 11卷 59142-59163页

作者： Dillan, Timothy Fudholi, Dhomas Hatta Univ Islam Indonesia Fac Ind Technol Dept Informat Yogyakarta 55584 Indonesia

Advancements in knowledge are pivotal to academic progress, necessitating efficient methods for discovering the state-of-the-art in various fields. Existing approaches, however, are language-specific and lack automation, limiting their efficacy. This study aims to develop a language-agnostic software that streamlines the process of identifying state-of-the-art research across diverse academic topics. The software automatically retrieves articles from multiple databases and preprocesses the content through tokenization, case folding, token cleansing, stopword removal, and lemmatization. Subsequently, a numeric document-phrase matrix is created and analyzed using latent Dirichlet allocation (LDA) and bidirectional encoder representations from transformers (BERT) to discover and label topics automatically. The study introduces a novel topic-filtering method based on entity linking and filtering model outputs using a knowledge database to ensure topic relevance. The visual representation employs nested bubble and line charts, effectively illustrating current topics, gaps, and research evolution trends. A user survey spread to 52 student researchers assessing the interface, topic relevance, and research output of the developed software, revealed that the interface is user-friendly, easy to navigate, and the presented information is comprehensible. Survey results also indicated that the generated topics are consistent with the processed article content and relevant to the investigated topic. The visualization effectively aids in understanding the state-of-the-art and research map. This study demonstrates that integrating LDA, BERT, and the proposed topic filtering and labeling method yields a robust tool for preliminary research analysis with high precision and relevance.

关键词： Topic modeling state-of-the-art discovery development of knowledge latent-dirichlet allocation bidirectional encoder representations from transformers entity linking

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Traditional Chinese Medicine Synonymous Term Conversion:A bidirectional encoder representations from transformers-Based Model for Converting Synonymous Terms in Traditional Chinese Medicine

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World Journal of Traditional Chinese Medicine 2023年第2期9卷 224-233页

作者： Lu Zhou Chao-Yong Wu Xi-Ting Wang Shuang-Qiao Liu Yi-Zhuo Zhang Yue-Meng Sun Jian Cui Cai-Yan Li Hui-Min Yuan Yan Sun Feng-Jie Zheng Feng-Qin Xu Yu-Hang Li School of Traditional Chinese Medicine Beijing University of Chinese MedicineChina Traditional Chinese Medicine(Zhong Jing)School Henan University of Chinese MedicineChina Shenzhen Hospital of Beijing University of Chinese Medicine China School of Life Sciences Beijing University of Chinese Medicine China Department of Medicine Columbia University Irving Medical CenterNew YorkUSA TCM Information Science Research Center School of Traditional Chinese MedicineBeijing University of Chinese MedicineChina Xiyuan Hospital of China Academy of Chinese Medical Sciences BeijingChina

Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of ***,there is a lack of models available to normalize synonymous TCM ***,construction of a synonymous term conversion(STC)model for normalizing synonymous TCM terms is ***:Based on the neural networks of bidirectional encoder representations from transformers(BERT),four types of TCM STC models were designed:Models based on BERT and text classification,text sequence generation,named entity recognition,and text *** superior STC model was selected on the basis of its performance in converting synonymous ***,three misjudgment inspection methods for the conversion results of the STC model based on inconsistency were proposed to find incorrect term conversion:Neuron random deactivation,output comparison of multiple isomorphic models,and output comparison of multiple heterogeneous models(OCMH).Results:The classification-based STC model outperformed the other STC task *** achieved F1 scores of 0.91,0.91,and 0.83 for performing symptoms,patterns,and treatments STC tasks,*** OCMH method showed the best performance in misjudgment inspection,with wrong detection rates of 0.80,0.84,and 0.90 in the term conversion results for symptoms,patterns,and treatments,***:The TCM STC model based on classification achieved superior performance in converting synonymous terms for symptoms,patterns,and *** misjudgment inspection method based on OCMH showed superior performance in identifying incorrect outputs.

关键词： bidirectional encoder representations from transformers misjudgment inspection synonymous term conversion traditional Chinesem edicine

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Optimizing federated learning approaches with hybrid Convolutional Neural Networks - bidirectional encoder representations from transformers for precise estimation of average localization errors in wireless sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第13期37卷 e5822-e5822页

作者： Lakshminarayanan, Raja Dhanasekaran, Selvaraj Kumar, Rangarajan Vinod Selvaraj, Aswath Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore 641202 Tamil Nadu India Saveetha Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India

Wireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network-bidirectional encoder representations from transformers (CNN-BERT) model. Effectively applying min-max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10;Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12;and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL-optimized method in improving localization accuracy. The outcomes demonstrate the FL-optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings. The main contribution of the research is the utilization of federated learning approaches with hybrid architecture that combines BERT and CNN for accurate ALE estimation in WSN. In order to forecast ALE with less computational complexity, the research introduces machine learning techniques

关键词： average localization errors bidirectional encoder representations from transformers convolutional neural network federated learning wireless sensor networks

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Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext plus CNN (Robustly Optimized bidirectional encoder representations from transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study

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JMIR MEDICAL INFORMATICS 2022年第4期10卷 142-152页

作者： Sun, Yuanyuan Gao, Dongping Shen, Xifeng Li, Meiting Nan, Jiale Zhang, Weining Chinese Acad Med Sci Peking Union Med Coll Inst Med Informat 3 Yabao Rd Beijing 100020 Peoples R China Chinese Acad Med Sci Peking Union Med Coll Hosp Dept Internal Med Beijing Peoples R China

Background: With the prevalence of online consultation, many patient-doctor dialogues have accumulated, which, in an authentic language environment, are of significant value to the research and development of intelligent question answering and automated triage in recent natural language processing studies. Objective: The purpose of this study was to design a front-end task module for the network inquiry of intelligent medical services. Through the study of automatic labeling of real doctor-patient dialogue text on the internet, a method of identifying the negative and positive entities of dialogues with higher accuracy has been explored. Methods: The data set used for this study was from the Spring Rain Doctor internet online consultation, which was downloaded from the official data set of Alibaba Tianchi Lab. We proposed a composite abutting joint model, which was able to automatically classify the types of clinical finding entities into the following 4 attributes: positive, negative, other, and empty. We adapted a downstream architecture in Chinese Robustly Optimized bidirectional encoder representations from transformers Pretraining Approach (RoBERTa) with whole word masking (WWM) extended (RoBERTa-WWM-ext) combining a text convolutional neural network (CNN). We used RoBERTa-WWM-ext to express sentence semantics as a text vector and then extracted the local features of the sentence through the CNN, which was our new fusion model. To verify its knowledge learning ability, we chose Enhanced Representation through Knowledge Integration (ERNIE), original bidirectional encoder representations from transformers (BERT), and Chinese BERT with WWM to perform the same task, and then compared the results. Precision, recall, and macro-F1 were used to evaluate the performance of the methods. Results: We found that the ERNIE model, which was trained with a large Chinese corpus, had a total score (macro-F1) of 65.78290014, while BERT and BERT-WWM had scores of 53.18247117 and 6

关键词： online consultation named entity automatic classification ERNIE Enhanced Representation through Knowledge Integration BERT bidirectional encoder representations from transformers machine learning neural network model China Chinese classification patient-physician dialogue patient doctor dialogue semantics natural language processing

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A Fine-Tuned bidirectional encoder representations from transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation

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JOURNAL OF MEDICAL INTERNET RESEARCH 2021年第8期23卷 e28229页

作者： Stojanov, Riste Popovski, Gorjan Cenikj, Gjorgjina Seljak, Barbara Korousic Eftimov, Tome Ss Cyril & Methodius Univ Skopje Fac Comp Sci & Engn Skopje North Macedonia Jozef Stefan Inst Comp Syst Dept Jamova Cesta 39 Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Ljubljana Slovenia

Background: Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drugs. In the last 2 decades, a large amount of work has been done in natural language processing and machine learning to enable biomedical information extraction. However, machine learning in food science domains remains inadequately resourced, which brings to attention the problem of developing methods for food information extraction. There are only few food semantic resources and few rule-based methods for food information extraction, which often depend on some external resources. However, an annotated corpus with food entities along with their normalization was published in 2019 by using several food semantic resources. Objective: In this study, we investigated how the recently published bidirectional encoder representations from transformers (BERT) model, which provides state-of-the-art results in information extraction, can be fine-tuned for food information extraction. Methods: We introduce FoodNER, which is a collection of corpus-based food named-entity recognition methods. It consists of 15 different models obtained by fine-tuning 3 pretrained BERT models on 5 groups of semantic resources: food versus nonfood entity, 2 subsets of Hansard food semantic tags, FoodOn semantic tags, and Systematized Nomenclature of Medicine Clinical Terms food semantic tags. Results: All BERT models provided very promising results with 93.30% to 94.31% macro F1 scores in the task of distinguishing food versus nonfood entity, which represents the new state-of-the-art technology in food information extraction. Considering the tasks where semantic tags are predicted, all BERT models obtained very promising results once again, with their macro F1 scores ranging from 73.39% to 78.96%. Conclusions: FoodNER can be used to extract and annotate

关键词： food information extraction named-entity recognition fine-tuning BERT semantic annotation information extraction BERT bidirectional encoder representations from transformers natural language processing machine learning

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Fine-Tuned bidirectional encoder representations from transformers Versus ChatGPT for Text-Based Outpatient Department Recommendation: Comparative Study

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JMIR Formative Research 2024年 8卷 e47814页

作者： Jo, Eunbeen Yoo, Hakje Kim, Jong-Ho Kim, Young-Min Song, Sanghoun Joo, Hyung Joon Department of Medical Informatics Korea University College of Medicine Seoul South Korea Department of Bio-Mechatronic Engineering Sungkyunkwan University College of Biotechnology and Bioengineering Gyeonggi South Korea Medical AI Research Center Research Institute for Future Medicine Samsung Medical Center Seoul South Korea Korea University Research Institute for Medical Bigdata Science Korea University Seoul South Korea Department of Cardiology Cardiovascular Center Korea University College of Medicine Seoul South Korea School of Interdisciplinary Industrial Studies Hanyang University Seoul South Korea Department of Linguistics Korea University Seoul South Korea

Background: Patients often struggle with determining which outpatient specialist to consult based on their symptoms. Natural language processing models in health care offer the potential to assist patients in making these decisions before visiting a hospital. Objective: This study aimed to evaluate the performance of ChatGPT in recommending medical specialties for medical questions. Methods: We used a dataset of 31,482 medical questions, each answered by doctors and labeled with the appropriate medical specialty from the health consultation board of NAVER (NAVER Corp), a major Korean portal. This dataset includes 27 distinct medical specialty labels. We compared the performance of the fine-tuned Korean Medical bidirectional encoder representations from transformers (KM-BERT) and ChatGPT models by analyzing their ability to accurately recommend medical specialties. We categorized responses from ChatGPT into those matching the 27 predefined specialties and those that did not. Both models were evaluated using performance metrics of accuracy, precision, recall, and F₁-score. Results: ChatGPT demonstrated an answer avoidance rate of 6.2% but provided accurate medical specialty recommendations with explanations that elucidated the underlying pathophysiology of the patient’s symptoms. It achieved an accuracy of 0.939, precision of 0.219, recall of 0.168, and an F₁-score of 0.134. In contrast, the KM-BERT model, fine-tuned for the same task, outperformed ChatGPT with an accuracy of 0.977, precision of 0.570, recall of 0.652, and an F₁-score of 0.587. Conclusions: Although ChatGPT did not surpass the fine-tuned KM-BERT model in recommending the correct medical specialties, it showcased notable advantages as a conversational artificial intelligence model. By providing detailed, contextually appropriate explanations, ChatGPT has the potential to significantly enhance patient comprehension of medical information, thereby improving the medical re

关键词： AI AI technology application artificial intelligence BERT bidirectional encoder representations from transformers chatbot ChatGPT conversational agent generative pretrained transformer health care health care application large language model medical specialty prediction natural language processing quality of care

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A Chinese medical named entity recognition method considering length diversity of entities

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 150卷

作者： Zhang, Hongyu Lyu, Long Chang, Weifu Zhao, Yuexin Peng, Xiaoqing Cent South Univ Sch Business Changsha 410083 Peoples R China Hunan Prov Key Lab Philosophy & Social Sci Urban S Changsha 410083 Peoples R China Cent South Univ Xiangya Hosp 3 Changsha 410083 Peoples R China

Extracting clinical entity concepts from professional medical materials is crucial for medical information analysis and knowledge extraction. Whereas, the Chinese medical named entity recognition (CMNER) task faces challenges due to the knowledge specialization and the diversity in entity lengths. To address these challenges, a novel method by considering length diversity of entities for CMNER is proposed, focusing on the integration of local information based on the predominance of large language models (LLMs). The method pre-trains a bidirectional encoder representation from transformers (BERT) based on open Chinese medical texts and designs a multi-dimensional convolutional residual module to enhance the semantic information for characters. This module effectively mines local information across various ranges and employs a local channel self-attention block to integrate this information, establishing a link between local information and entity length. Meanwhile, an adaptive optimization strategy for a learning rate is designed to improve the method's ability to search for the optimal solution. Experimental results reveal that, compared with state-of-the-art models, our approach achieves the optimal Recall and F1, especially Recalls achieve 94.50 % (p < 0.05) and 93.51 % (p < 0.05) with effective performance in current task. The ablation results suggest that incorporating local information within 1-7 characters effectively addresses the challenges mentioned, highlighting the potential of our method to advance CMNER task.

关键词： Chinese medical named entity recognition Entity length Local information Multi-sized convolutional neural network bidirectional encoder representations from transformers Adaptive optimization strategy for learning rate

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TraitBertGCN: Personality Trait Prediction Using BertGCN with Data Fusion Technique

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷

作者： Waqas, Muhammad Zhang, Fengli Laghari, Asif Ali Almadhor, Ahmad Petrinec, Filip Iqbal, Asif Khalil, Mian Muhammad Yasir Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China Network & Data Secur Key Lab Sichuan Prov Chengdu Peoples R China Shenyang Normal Univ Software Coll Shenyang Peoples R China Jouf Univ Coll Comp & Informat Sci Dept Comp Engn & Networks Sakaka 72388 Saudi Arabia Comenius Univ Fac Management Dept Informat Management & Business Syst Odbojarov 10 Bratislava 25 Slovakia

Personality prediction via different techniques is an established and trending topic in psychology. The advancement of machine learning algorithms in multiple fields also attracted the attention of Automatic Personality Prediction (APP). This research proposes a novel TraitBertGCN method with a data fusion technique for predicting personality traits. Initially, this work integrates a pre-trained language model, bidirectional encoder representations from transformers (BERT), with a three-layer Graph Convolutional Network (GCN) to leverage large-scale language understanding and graph-based learning for personality prediction. This study fuses the two datasets (essays and myPersonality) to overcome the bias and generalize the model across different domains. We fine-tuned our TraitBertGCN model on the fused dataset and then evaluated it on both datasets individually to assess its adaptability and accuracy in varied contexts. We compared the proposed model's results with previous studies;our model achieved better performance in personality trait prediction across multiple datasets, with an average accuracy of 77.42% on the essays dataset and 87.59% on the myPersonality dataset.

关键词： Automatic personality prediction bidirectional encoder representations from transformers Graph convolutional network TraitBertGCN Data fusion

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Enhanced Emotion-Aware Conversational Agent: Analyzing User Behavioral Status for Tailored Reponses in Chatbot Interactions

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IEEE ACCESS 2025年 13卷 19770-19787页

作者： Abinaya, S. Ashwin, K. S. Alphonse, A. Sherly Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

While chatbots are increasingly popular for communication, their effectiveness is limited by their difficulty in understanding users' emotions. To address this, this study proposes a new hybrid chatbot model called "TEBC-Net" (Text Emotion Bert CNN Network), which combines text and video analysis to interpret user emotions and generate more empathetic responses. At the core of TEBC-Net is a multi-modal emotion analysis system. One component uses bidirectional encoder representations from transformers (BERT), a well-regarded model in natural language processing (NLP), achieving an 87.21% accuracy rate in detecting emotional cues from text inputs. The second component captures users' facial expressions through webcam footage. It begins by detecting faces using a pre-trained classifier like Haarcascade. Then, to improve emotion recognition, it preprocesses the image through brightness adjustments and contrast enhancement with Automatic CLAHE and dual gamma correction. This processed image is analyzed by a Convolutional Neural Network (CNN) model trained specifically for emotion recognition, reaching 74.14% accuracy by assigning probabilities to different emotions. By integrating insights from both text and video analysis, TEBC-Net gains a comprehensive understanding of the user's emotional state and intent. This combined data then informs the chatbot's response generation module, enabling it to craft responses that are both empathetic and more directly aligned with the user's emotional needs.

关键词： Chatbots Emotion recognition Encoding bidirectional control Convolutional neural networks Accuracy Faces Classification algorithms transformers Training Chatbot bidirectional encoder representations from transformers natural language processing emotion recognition automatic CLAHE with dual gamma correction convolutional neural network

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