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Adapting transformer-based language models for heart disease detection and risk factors extraction

JOURNAL OF BIG DATA 2024年第1期11卷 47页

作者： Houssein, Essam H. Mohamed, Rehab E. Hu, Gang Ali, Abdelmgeid A. Minia Univ Fac Comp & Informat Al Minya Egypt Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China

Efficiently treating cardiac patients before the onset of a heart attack relies on the precise prediction of heart disease. Identifying and detecting the risk factors for heart disease such as diabetes mellitus, Coronary Artery Disease (CAD), hyperlipidemia, hypertension, smoking, familial CAD history, obesity, and medications is critical for developing effective preventative and management measures. Although Electronic Health Records (EHRs) have emerged as valuable resources for identifying these risk factors, their unstructured format poses challenges for cardiologists in retrieving relevant information. This research proposed employing transfer learning techniques to automatically extract heart disease risk factors from EHRs. Leveraging transfer learning, a deep learning technique has demonstrated a significant performance in various clinical natural language processing (NLP) applications, particularly in heart disease risk prediction. This study explored the application of transformer-based language models, specifically utilizing pre-trained architectures like BERT (bidirectional encoder representations from transformers), RoBERTa, BioClinicalBERT, XLNet, and BioBERT for heart disease detection and extraction of related risk factors from clinical notes, using the i2b2 dataset. These transformer models are pre-trained on an extensive corpus of medical literature and clinical records to gain a deep understanding of contextualized language representations. Adapted models are then fine-tuned using annotated datasets specific to heart disease, such as the i2b2 dataset, enabling them to learn patterns and relationships within the domain. These models have demonstrated superior performance in extracting semantic information from EHRs, automating high-performance heart disease risk factor identification, and performing downstream NLP tasks within the clinical domain. This study proposed fine-tuned five widely used transformer-based models, namely BERT, RoBERTa, BioClini

关键词： Coronary artery disease Electronic health records Natural language processing bidirectional encoder representations from transformers Heart disease Transformer-based models

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Enhanced Topic-Aware Summarization Using Statistical Graph Neural Networks

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Computers, Materials & Continua 2024年第8期80卷 3221-3242页

作者： Ayesha Khaliq Salman Afsar Awan Fahad Ahmad Muhammad Azam Zia Muhammad Zafar Iqbal Department of Computer Science University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan School of Computing Faculty of TechnologyUniversity of PortsmouthSouthseaPortsmouthPO12UPUK Department of Mathematics and Statistics University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original *** approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent *** study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based bidirectional encoder Representa-tions from transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical *** approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text *** model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and *** results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document *** research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

关键词： Summarization graph attention network bidirectional encoder representations from transformers Latent Dirichlet Allocation term frequency-inverse document frequency

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Legal information extraction and classification using BERT, Bi-LSTM, and CRF models

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Li, Jingwei Xi An Jiao Tong Univ Coll Law 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

This article combined BERT (bidirectional encoder Representation from transformers), Bi-LSTM (bidirectional Long Short-Term Memory), and CRF (Conditional Random Field) models to transform unstructured legal text into structured data through information extraction, improving the effectiveness of legal information extraction. The BERT model can be used for deep semantic embedding of legal texts, generating context-sensitive representations for each word. The Bi-LSTM network can capture long-distance dependencies in the text, extract sequence features, and apply CRF layers to globally optimize sequence labels to ensure accurate annotation of entity boundaries and relationships. In the dataset for extracting legal entity relationships related to prostitution constructed in this article, the accuracy, precision, recall rate, and F1 score of entity classification reached 93.6%, 92.7%, 92.1%, and 92.4%, respectively. All 153 samples in the Engage_in_ prostitution relationship were correctly classified. In order to analyze the stability of legal information extraction and classification, the model proposed by this article was tested on five datasets: CAIL2019, CJRC (Chinese Judicial Reading Comprehension), LexGLUE (Legal General Language Understanding Evaluation), COLIEE (Competition on Legal Information Extraction/Appointment), and ECHR (European Court of Human Rights). The accuracy of the article's model fluctuated only 1.2% on different datasets, while the precision remained stable and the recall fluctuated by 0.7%. This article provided reliable technical support for legal intelligence research by combining BERT, Bi-LSTM, and CRF to accurately extract and classify legal information.

关键词： legal information extraction relation classification bidirectional encoder representations from transformers bidirectional long short-term memory conditional random field prostitution laws

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Preschool language development prediction model based on deep learning: Cross-cultural validity evaluation

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Sun, Yuanyuan Huo, Jiasong ZhongYuan Inst Sci & Technol Fac Educ & Art Zhengzhou Peoples R China Zhengzhou Univ Sch Management 100 Kexue Ave Zhengzhou 450001 Peoples R China

Traditional research on preschool language development often fails to capture the complex nonlinear relationships and high-dimensional characteristics of language growth, leading to low prediction accuracy and poor cross-cultural applicability. This paper introduces a novel BERT (bidirectional encoder representations from transformers)-based model to predict preschool language development and evaluate its cross-cultural effectiveness. Text data from preschool children's language datasets across multiple cultural backgrounds is collected, cleaned, and preprocessed to create suitable training samples. Special attention is given to the unique grammatical structures and cultural expressions in each language to ensure compatibility with the model. The BERT model is used to encode the processed text, leveraging its bidirectional self-attention mechanism to extract contextual information and generate deep feature representations essential for understanding preschool language development. The model combines both grammatical and semantic features for meaningful representations in subsequent predictions. Fine-tuning the pre-trained BERT model using the Adam optimizer enhances prediction accuracy, while cross-validation and hyperparameter tuning further improve its performance. Culturally specific annotations and vocabularies are incorporated to ensure the model's effective prediction of language development across different regions. Experimental results show that the BERT model achieves an MAE (Mean Absolute Error) between 0.20 and 0.25, an MSE (Mean Squared Error) between 0.05 and 0.08, and an average R2 value of 0.84 across English, Chinese, Spanish, and Japanese. These results demonstrate the model's high accuracy and strong cross-cultural stability in predicting preschool language development.

关键词： preschool language language development prediction prediction model deep learning bidirectional encoder representations from transformers

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Using transformer-based models and social media posts for heat stroke detection

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SCIENTIFIC REPORTS 2025年第1期15卷 1-9页

作者： Anno, Sumiko Kimura, Yoshitsugu Sugita, Satoru Sophia Univ Grad Sch Global Environm Studies Tokyo Japan Yanagi Pearls Shima Mie Japan Chubu Univ Chubu Inst Adv Studies Kasugai Aichi Japan

Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for the early detection of health crises and affected individuals, including those related to infectious diseases. However, the reliability of such posts, being subjective and not clinically diagnosed, remains a challenge. In this study, we address this issue by assessing the classification performance of transformer-based pretrained language models to accurately classify Japanese tweets related to heat stroke, a significant health effect of climate change, as true or false. We also evaluated the efficacy of combining SNS and artificial intelligence for event-based public health surveillance by visualizing the data on correctly classified tweets and heat stroke emergency medical evacuees in time-space and animated video, respectively. The transformer-based pretrained language models exhibited good performance in classifying the tweets. Spatiotemporal and animated video visualizations revealed a reasonable correlation. This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks.

关键词： Event-based surveillance bidirectional encoder representations from transformers Language understanding with knowledge-based embeddings Tweets Heat stroke

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The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review

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JMIR MEDICAL INFORMATICS 2024年 12卷 e53787页

作者： Preiksaitis, Carl Ashenburg, Nicholas Bunney, Gabrielle Chu, Andrew Kabeer, Rana Riley, Fran Ribeira, Ryan Rose, Christian Stanford Univ Sch Med Dept Emergency Med 900 Welch Rd Suite 350 Palo Alto CA 94304 USA

Background: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in Objective: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. Methods: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. Results: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency;(2) efficiency, workflow, and information management demonstrat

关键词： large language model LLM emergency medicine clinical decision support workflow efficiency medical education artificial intelligence AI natural language processing NLP AI literacy ChatGPT Bard Pathways Language Model Med-PaLM bidirectional encoder representations from transformers BERT generative pretrained transformer GPT United States US China scoping review Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA decision support workflow efficiency risk ethics education communication medical training physician health literacy emergency care

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Exploring Public Emotions on Obesity During the COVID-19Pandemic Using Sentiment Analysis and Topic Modeling:Cross-Sectional Study

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JOURNAL OF MEDICAL INTERNET RESEARCH 2024年 26卷 e52142页

作者： Correia, Jorge Cesar Ahmad, Sarmad Shaharyar Waqas, Ahmed Meraj, Hafsa Pataky, Zoltan Univ Hosp Geneva WHO Collaborating Ctr Unit Therapeut Patient Educ Chemin Venel 7 CH-1206 Geneva Switzerland Univ Geneva Chemin Venel 7 CH-1206 Geneva Switzerland Liverpool Hope Univ Sch Math Comp Sci & Engn Liverpool England Univ Liverpool Inst Populat Hlth Dept Primary Care & Mental Hlth Liverpool England Greater Manchester Mental Hlth NHS Fdn Trust Salford England

Background: Obesity is a chronic, multifactorial, and relapsing disease, affecting people of all ages worldwide, and is directly related to multiple complications. Understanding public attitudes and perceptions toward obesity is essential for developing effective health policies, prevention strategies, and treatment approaches. Objective: This study investigated the sentiments of the general public, celebrities, and important organizations regarding obesity using social media data, specifically from Twitter (subsequently rebranded as X).Methods: The study analyzes a dataset of 53,414 tweets related to obesity posted on Twitter during the COVID-19 pandemic, from April 2019 to December 2022. Sentiment analysis was performed using the XLM-RoBERTa-base model, and topic modeling was conducted using the BERTopic library. Results: The analysis revealed that tweets regarding obesity were predominantly negative. Spikes in Twitter activity correlated with significant political events, such as the exchange of obesity-related comments between US politicians and criticism of theUnited Kingdom's obesity campaign. Topic modeling identified 243 clusters representing various obesity-related topics, such as childhood obesity;the US President's obesity struggle;COVID-19 vaccinations;the UK government's obesity campaign;body shaming;racism and high obesity rates among Black American people;smoking, substance abuse, and alcohol consumption among people with obesity;environmental risk factors;and surgical treatments. Conclusions: Twitter serves as a valuable source for understanding obesity-related sentiments and attitudes among the public, celebrities, and influential organizations. Sentiments regarding obesity were predominantly negative. Negative portrayals of obesity by influential politicians and celebrities were shown to contribute to negative public sentiments, which can have adverse effects on public health. It is essential for public figures to be mindful of their impact on publ

关键词： obesity Twitter infodemic attitude opinion perception perspective obese weight overweight social media tweet sentiment topic modeling BERT bidirectional encoder representations from transformers NLP natural language processing generalpublic celebrities

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CGDGMDA-Net: discovering microbe-disease and drug associations through CTGAN and graph-based deep learning

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2024年第1期13卷 48页

作者： Naik, Anushka Patwardhan, Ishan Joshi, Amit COEP Technol Univ COEP Tech Dept Comp Sci & Engn Wellesley Rd Pune 411005 Maharashtra India

This research proposes a novel method for enhancing the prediction of microbe-disease associations and the identification of possible drug treatments through the utilization of advanced computational strategies. The foundation of this approach is the integration of data from the Human Microbe-Disease Association Database with advanced techniques such as Conditional Tabular Generative Adversarial Networks for augmenting data and a combination of Graph Neural Networks and Graph Attention Networks for modeling the intricate relationships between microbes and diseases. Additionally, this study leverages the bidirectional encoder representations from transformers for the encoding of diseases and drugs, thereby improving the prediction capabilities for disease-drug associations. A thorough experimental analysis validates the efficacy of these approaches in decoding complex biological datasets, offering valuable insights into disease etiology and potential treatment pathways. The results highlight the significance of merging generative models, graph-based deep learning, and transformer-based natural language processing models to push forward the boundaries of biomedical research and its applications.

关键词： Graph neural networks Graph attention networks Disease-drug associations Generative adversarial network bidirectional encoder representations from transformers

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Aspect term extraction and optimized deep learning for sentiment classification

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SOCIAL NETWORK ANALYSIS AND MINING 2024年第1期14卷 221页

作者： Adilakshmi, Konda Srinivas, Malladi Anuradha, K. Srilakshmi, V. Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Prades India Gokaraju Rangaraju Inst Engn & Technol GRIET Dept CSE Hyderabad 500090 India

Sentiment or opinion largely relies on public commentary, where reflections are either positive or negative. Sentiment classification automates the process of determining the orientation of a subject based on text, which aims to classify documents by expressed views. This task faces significant challenges due to issues, like negation, ambiguity, and complex language structures. In this work, a new Squirrel Search Mayfly Algorithm_Hierarchical Deep Learning for Text (SSMA_HDLTex) is established for sentiment classification using aspect term extraction and optimized deep learning. Primarily, the document on Amazon review is taken as input and later it is applied to the process of tokenization. In this process, bidirectional encoder representations from transformers is employed to partition the sentence into tokens. Later, Aspect Term Extraction is effectuated. At last, sentiment classification is done by employing HDLTex which is trained by utilizing the presented SSMA method. The novel SSMA method is devised by integrating the Squirrel Search Algorithm as well as the Mayfly Algorithm. The proposed SSMA_HDLTex has attained maximal and precision, F-measure, and recall, of 0.936, 0.937, and 0.941correspondingly. This innovative approach significantly enhances the accuracy and reliability of sentiment classification tasks.

关键词： Squirrel search algorithm Mayfly optimization algorithm bidirectional encoder representations from transformers Squirrel search Mayfly algorithm Hierarchical deep learning for text

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GBRE-AHB: contextual understanding for cross-domain aspect categorization with adaptive hyperparameter tuning

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 43-55页

作者： Kumaragurubaran, T. Chiranjeevi, V. Rahul Elangovan, D. Kumar, S. Vinodh Rajalakshmi Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Panimalar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

The cross-domain aspect detection and categorization is a vital task in natural language processing, enabling the automated identification and categorization of aspects in textual data spanning diverse domains. Traditional methods face several complexities such as scalability, limited contextual understanding of words, data sparsity, and adaptation difficulty. So, a novel method named gated bidirectional recurrent encoder-based adaptive honey badger (GBRE-AHB) algorithm is proposed for cross-domain aspect detection and categorization. In this study, the bidirectional encoder representations for transformers (BERT) is utilized to capture contextual information from text and enable better aspect identification and categorization by understanding the context. The local optimization problems are identified and solved by determining an adaptive strategy. Also, the gated recurrent unit (GRU) is employed to sequence the text data and allow aspect detection by considering the sequence in which aspects appear within a document. The study is validated on the datasets, namely the IMDB dataset of 50 K movie reviews and the cell phone reviews sentiment analysis-body dataset. The efficiency is validated by various metrics that attained the ranges as F1-score (97.85%), specificity (97.83%), recall (97.82%), precision (97.94%), and accuracy (98.67%), respectively. The experimental results revealed that the proposed method for cross-domain aspect detection and categorization as well as improved the reliability as well as longevity of the model and determined the impacts applied in the categorization process.

关键词： Cross-domain aspect detection bidirectional encoder representations from transformers Gated recurrent unit Honey badger optimization Adaptive strategy Stemming Tokenization Lemmatization Stop word removal

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