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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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TRCaptionNet: A novel and accurate deep Turkish image captioning model with vision transformer based image encoders and deep linguistic text decoders

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2023年第6期31卷 1079-1098页

作者： Yildiz, Serdar Memis, Abbas Varli, Songul Yildiz Tech Univ Fac Elect & Elect Engn Dept Comp Engn Istanbul Turkiye Hlth Inst Turkiye Istanbul Turkiye TUBITAK BILGEM Kocaeli Turkiye

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, a feature projection module based on vision transformers, and a text decoder. In the first stage, the system encodes the input images via the CLIP (contrastive language-image pretraining) image encoder. The CLIP image features are then passed through a vision transformer and the final image features to be linked with the textual features are obtained. In the last stage, a deep text decoder exploiting a BERT (bidirectional encoder representations from transformers) based model is used to generate the image cations. Furthermore, unlike the related works, a natural language-based linguistic model called NLLB (No Language Left Behind) was employed to produce Turkish captions from the original English captions. Extensive performance evaluation studies were carried out and widely known image captioning quantification metrics such as BLEU, METEOR, ROUGE-L, and CIDEr were measured for the proposed model. Within the scope of the experiments, quite successful results were observed on MS COCO and Flickr30K datasets, two known and prominent datasets in this field. As a result of the comparative performance analysis by taking the existing reports in the current literature on TIC into consideration, it was witnessed that the proposed model has superior performance and outperforms the related works on TIC so far. Project details and demo links of TRCaptionNet wil

关键词： Image captioning image understanding Turkish image captioning contrastive language-image pretraining bidirectional encoder representations from transformers image and natural language processing

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Exploratory Data Analysis and Classification of a New Arabic Online Extremism Dataset

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IEEE ACCESS 2021年 9卷 161613-161626页

作者： Aldera, Saja Emam, Ahmed Al-Qurishi, Muhammad Alrubaian, Majed Alothaim, Abdulrahman King Saud Univ Coll Business Adm Management Informat Syst Dept Riyadh 11451 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11451 Saudi Arabia

The dissemination of extremist ideas and causes online has intensified over the last decade. Extremist organizations use social media to gain publicity and new recruits, often with little interference from network providers. New techniques are being developed to identify extremist content, ensuring it can be promptly removed and its authors blocked from network access. However, most techniques are only compatible with the English language, despite the fact that extremist propaganda is frequently shared in other languages, including Arabic. Since the most effective methods for automated linguistic analysis use deep learning and require large, high-quality datasets, creating specialised data samples containing examples of extremist communication is an essential step toward a practical solution. In this paper, we present a dataset compiled for this purpose and discuss the classification methods that can be used for extremism detection. The manually annotated Arabic Twitter dataset consists of 89,816 tweets published between 2011 and 2021. Using guidelines, three expert annotators labelled the tweets as extremist or non-extremist. Exploratory data analysis was performed to understand the dataset's features. Classification algorithms were used with the dataset, including logistic regression, support vector machine, multinominal naive Bayes, random forest, and BERT. Among the traditional machine learning models, support vector machine with term frequency-inverse document frequency features achieved the highest accuracy (0.9729). However, BERT outperformed the traditional models with an accuracy of 0.9749. This dataset is expected to enhance the accuracy of Arabic online extremism classification in future research, and so we have made it publicly available.

关键词： Social networking (online) Blogs Support vector machines Data analysis Radio frequency Feature extraction Psychology Extremism radicalization benchmark dataset exploratory data analysis machine learning bidirectional encoder representations from transformers

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Elevating Offensive Language Detection: CNN-GRU and BERT for Enhanced Hate Speech Identification

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第5期15卷 1164-1172页

作者： Madhavi, M. Agal, Sanjay Odedra, Niyati Dhirubhai Chowdhary, Harish Ruprah, Taranpreet Singh Vuyyuru, Veera Ankalu El-Ebiary, Yousef A. Baker Velagapudi Ramakrishna Siddhartha Engn Coll Dept CSE Vijayawada Andhra Pradesh India Parul Inst Engn & Technol PIET Dept Comp Sci & Engn PO Limda Ta Waghodia 391760 Gujarat India Dr V R Godhania Coll Engn & Technol Dept Comp Engn Porbandar Gujarat India Rashtriya Raksha Univ Gandhinagar Gujarat India Rajarambapu Inst Technol Sakharale Sakharale India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Andhra Pradesh India UniSZA Univ Fac Informat & Comp Kuala Terengganu Malaysia

Upholding a secure and accepting digital environment is severely hindered by hate speech and inappropriate information on the internet. A novel approach that combines Convolutional Neural Network with GRU and BERT from transformers proposed for enhancing the identification of offensive content, particularly hate speech. The method utilizes the strengths of both CNN-GRU and BERT models to capture complex linguistic patterns and contextual information present in hate speech. The proposed model first utilizes CNN-GRU to extract local and sequential features from textual data, allowing for effective representation learning of offensive language. Subsequently, BERT, advanced transformer-based model, is employed to capture contextualized representations of the text, thereby enhancing the understanding of detailed linguistic nuances and cultural contexts associated with hate speech. Fine tuning BERT model using hugging face transformer. To execute tests using datasets for hate speech identification that are made accessible to the public and show how well the method works to identify inappropriate content. By assisting with the continuing efforts to prevent the dissemination of hate speech and undesirable language online, the proposed framework promotes a more diverse and secure digital environment. The proposed method is implemented using python. The method achieves 98% competitive performance compared to existing approaches LSTM and RNN, CNN, LSTM and GBAT, showcasing its potential for real-world applications in combating online hate speech. Furthermore, it provides insights into the interpretability of the model's predictions, highlighting key linguistic and contextual factors influencing offensive language detection. The study contributes to advancing hate speech detection by integrating CNN-GRU and BERT models, giving a robust solution for enhancing offensive content identification in online platforms.

关键词： bidirectional encoder representations from transformers convolutional neural network Gated Recurrent Unit hate speech hugging face transformer

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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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Natural Language Processing for Automated Classification of Qualitative Data from Interviews of Patients With Cancer

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VALUE IN HEALTH 2022年第12期25卷 1995-2002页

作者： Fang, Chao Markuzon, Natasha Patel, Nikunj Rueda, Juan-David AstraZeneca Oncol Biometr ML AI 760 Winter St Waltham MA 02451 USA AstraZeneca US Med Affairs Gaithersburg MD USA AstraZeneca Oncol Market Access & Pricing Gaithersburg MD USA

Objectives: This study sought to explore the use of novel natural language processing (NLP) methods for classifying unstructured, qualitative textual data from interviews of patients with cancer to identify patient-reported symptoms and impacts on quality of life. Methods: We tested the ability of 4 NLP models to accurately classify text from interview transcripts as "symptom," "quality of life impact" and "other." Interview data sets from patients with hepatocellular carcinoma (HCC) (n = 25), biliary tract cancer (BTC) (n = 23), and gastric cancer (n = 24) were used. Models were cross-validated with transcript subsets designated for training, validation, and testing. Multiclass classification performance of the 4 models was evaluated at paragraph and sentence level using the HCC testing data set and analyzed by the one-versus-rest technique quantified by the receiver operating characteristic area under the curve (ROC AUC) score. Results: NLP models accurately classified multiclass text from patient interviews. The bidirectional encoder representations from transformers model generally outperformed all other models at paragraph and sentence level. The highest predictive performance of the bidirectional encoder representations from transformers model was observed using the HCC data set to train and BTC data set to test (mean ROC AUC, 0.940 [SD 0.028]), with similarly high predictive performance using balanced and imbalanced training data sets from BTC and gastric cancer populations. Conclusions: NLP models were accurate in predicting multiclass classification of text from interviews of patients with cancer, with most surpassing 0.9 ROC AUC at paragraph level. NLP may be a useful tool for scaling up processing of patient interviews in clinical studies and, thus, could serve to facilitate patient input into drug development and improving patient care.

关键词： bidirectional encoder representations from transformers natural language processing patient interviews patient-reported outcomes

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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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Deep Learning-based Natural Language Processing Methods Comparison for Presumptive Detection of Cyberbullying in Social Networks

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第5期12卷 796-803页

作者： Andrade-Segarra, Diego A. Leon-Paredes, Gabriel A. Univ Politecn Salesiana Grp Invest Cloud Comp Smart Cities & High Perform Cuenca 010102 Ecuador

Due to TIC development in the last years, users have managed to satisfy many social experiences through several digital media like blogs, web and especially social networks. However, not all social media users have had good experiences with these media. Since there are malicious users that are able to cause negative psychological effects over people, this is called cyberbullying. For this reason, social networks such as Twitter are looking to implement models based on deep learning or machine learning capable of recognizing harassing comments on their platforms. However, most of these models are focused on the use of English language and there are very few models adapted for Spanish language. This is why, in this paper we propose the evaluation of an RNN+LSTM neural network, as well as a BERT model through sentiment analysis, to perform the detection of cyberbullying based on Spanish language for Ecuadorian accounts of the social network Twitter. The results obtained show a balance between execution time and accuracy obtained for the RNN + LSTM model. In addition, evaluations of comments that are not explicitly offensive show a better performance for the BERT model, which outperforms its counterpart by 20%.

关键词： bidirectional encoder representations from transformers BERT Cyberbullying Natural Language Processing Recurrent Neural Network + Long Short Term Memory RNN+LSTM Sentiment Analysis Semantics Spanish Language Processing

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