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Designing a deep learning-based application for detecting fake online reviews

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 134卷

作者： Thao, Le Quang Kien, Do Trung Thuy, Dang Thi Thanh Thuy, Luong Thi Minh Bach, Ngo Chi Duc, Tran Tri Bach, Hoang Gia Cuong, Duong Duc VNU Univ Sci Fac Phys Hanoi 100000 Vietnam Vietnam Natl Univ Hanoi 100000 Vietnam Nguyen Tat Thanh High Sch Hanoi 100000 Vietnam HNUE High Sch Gifted Hanoi 100000 Vietnam

In the era of prevalent online commerce, online reviews significantly influence purchasing decisions. Unfortunately, this has also led to the emergence of fake reviews, which can deceive consumers and undermine trust in online platforms. Our study addresses this issue by developing DenyBERT, a deep learning-based software that enhances the bidirectional encoder representations from transformers (BERT) framework with Deep and Light Transformation (DeLighT) and Knowledge Distillation (KD) techniques. These innovations not only reduce computational demands but also improve the model 's accuracy in identifying fake reviews, making it ideally suited for real-world applications. Significantly, DenyBERT requires only 16.01M parameters-significantly fewer than its predecessors such as BERT and TinyBERT-yet achieves a robust accuracy of 96.12% and an F1-score of 96.47%. This efficiency makes it particularly suited for deployment on devices with limited processing capabilities. The software, developed in Python, features a flexible input mechanism that allows for analyzing reviews directly from websites via URL or via manual input of review paragraphs. Our findings indicate that DenyBERT outperforms existing models in both speed and accuracy, making it a powerful tool for combating fake reviews in real -time scenarios. This advancement not only enhances user trust in online review systems but also supports ecommerce platforms in maintaining a fair and transparent market environment.

关键词： Fake online review Natural language processing bidirectional encoder representations from transformers Deep and light transformation Knowledge distillation Single head-attention

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Enabling Remote School Education using Knowledge Graphs and Deep Learning Techniques

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Procedia Computer Science 2022年 215卷 618-625页

作者： Lekshmi S Nair M K Shivani Sr Jo Cheriyan Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri-690546 Kerala India Department of Computer Science and Engineering Saintgits College of Engineering Kottayam-686532 Kerala India

An automated question-answering system allows students to learn as an integral part of digitized learning. This system responds to queries using text. We also include a knowledge graph, which significantly enhances the model's intrigue and improves learners’ understanding. The features of knowledge entity extraction, information point evaluation and analysis, knowledge graph construction from unstructured text, and knowledge entity integration are all explored. The question-answering paradigm we suggest in this study uses knowledge graphs and BERT (bidirectional encoder representations from transformers) to provide diverse learners with quick feedback on the subject. In order to facilitate non-native learners’ understanding, we also include an English to Hindi translation. As a result, access to and continued learning can be very beneficial for educators.

关键词： Knowledge Graph bidirectional encoder representations from transformers Long Short-Term Memory Natural Language Processing Machine Translation Question-Answering Remote Education

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Automated Recognition of Visual Acuity Measurements in Ophthalmology Clinical Notes Using Deep Learning

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OPHTHALMOLOGY SCIENCE 2024年第2期4卷 100371-100371页

作者： Bernstein, Isaac A. Koornwinder, Abigail Hwang, Hannah H. Wang, Sophia Y. Stanford Univ Byers Eye Inst Dept Ophthalmol Palo Alto CA USA Weill Cornell Med Dept Ophthalmol New York NY USA 2370 Watson Ct Palo Alto CA 94030 USA

Purpose: Visual acuity (VA) is a critical component of the eye examination but is often only documented in electronic health records (EHRs) as unstructured free-text notes, making it challenging to use in research. This study aimed to improve on existing rule-based algorithms by developing and evaluating deep learning models to perform named entity recognition of different types of VA measurements and their lateralities from free-text ophthalmology notes: VA for each of the right and left eyes, with and without glasses correction, and with and without pinhole. Design: Cross-sectional study. Subjects: A total of 319 756 clinical notes with documented VA measurements from approximately 90 000 patients were included. Methods: The notes were split into train, validation, and test sets. bidirectional encoder representations from transformers (BERT) models were fine-tuned to identify VA measurements from the progress notes and included BERT models pretrained on biomedical literature (BioBERT), critical care EHR notes (ClinicalBERT), both (BlueBERT), and a lighter version of BERT with 40% fewer parameters (DistilBERT). A baseline rule-based al-gorithm was created to recognize the same VA entities to compare against BERT models. Main Outcome Measures: Model performance was evaluated on a held-out test set using microaveraged precision, recall, and F1 score for all entities. Results: On the human-annotated subset, BlueBERT achieved the best microaveraged F1 score (F1 = 0.92), followed by ClinicalBERT (F1 = 0.91), DistilBERT (F1 = 0.90), BioBERT (F1 = 0.84), and the baseline model (F1 = 0.83). Common errors included labeling VA in sections outside of the examination portion of the note, difficulties labeling current VA alongside a series of past VAs, and missing nonnumeric VAs. Conclusions: This study demonstrates that deep learning models are capable of identifying VA measurements from free-text ophthalmology notes with high precision and recall, achieving significant perfor

关键词： Deep learning Electronic health records Natural language processing Ophthalmology Visual acuity BERT bidirectional encoder representations from transformers EHR electronic health record NER named entity recognition OD right eye OS left eye TOVA Total VA Extraction Algorithm VA visual acuity

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A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media

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INFORMATION 2021年第8期12卷 331页

作者： Alexandridis, Georgios Varlamis, Iraklis Korovesis, Konstantinos Caridakis, George Tsantilas, Panagiotis Univ Aegean Dept Cultural Technol & Commun Mitilini 81100 Greece Harokopio Univ Athens Dept Informat & Telemat Kallithea 17671 Greece Palo Serv Ltd Athens 10562 Greece

As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources;nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community.

关键词： sentiment analysis opinion mining bidirectional encoder representations from transformers text embeddings transformers Greek social media

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Supervised Relation Extraction Between Suicide-Related Entities and Drugs: Development and Usability Study of an Annotated PubMed Corpus

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JOURNAL OF MEDICAL INTERNET RESEARCH 2023年第1期25卷 e41100页

作者： Karapetian, Karina Jeon, Soo Min Kwon, Jin-Won Suh, Young-Kyoon Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea Jeju Natl Univ Coll Pharm Jeju South Korea Kyungpook Natl Univ Coll Pharm Commun Based Intelligent Novel Drug Discovery Educ Daegu South Korea Kyungpook Natl Univ Res Inst Pharmaceut Sci Daegu South Korea Kyungpook Natl Univ Sch Comp Sci & Engn Rm 520 IT-5 80 Daehak-ro Daegu 41566 South Korea

Background: Drug-induced suicide has been debated as a crucial issue in both clinical and public health research. Published research articles contain valuable data on the drugs associated with suicidal adverse events. An automated process that extracts such information and rapidly detects drugs related to suicide risk is essential but has not been well established. Moreover, few data sets are available for training and validating classification models on drug-induced suicide. Objective: This study aimed to build a corpus of drug-suicide relations containing annotated entities for drugs, suicidal adverse events, and their relations. To confirm the effectiveness of the drug-suicide relation corpus, we evaluated the performance of a relation classification model using the corpus in conjunction with various embeddings. Methods: We collected the abstracts and titles of research articles associated with drugs and suicide from PubMed and manually annotated them along with their relations at the sentence level (adverse drug events, treatment, suicide means, or miscellaneous). To reduce the manual annotation effort, we preliminarily selected sentences with a pretrained zero-shot classifier or sentences containing only drug and suicide keywords. We trained a relation classification model using various bidirectional encoder representations from Transformer embeddings with the proposed corpus. We then compared the performances of the model with different bidirectional encoder representations from Transformer-based embeddings and selected the most suitable embedding for our corpus. Results: Our corpus comprised 11,894 sentences extracted from the titles and abstracts of the PubMed research articles. Each sentence was annotated with drug and suicide entities and the relationship between these 2 entities (adverse drug events, treatment, means, and miscellaneous). All of the tested relation classification models that were fine-tuned on the corpus accurately detected sentences of su

关键词： suicide adverse drug events information extraction relation classification bidirectional encoder representations from transformers pharmacovigilance natural language processing PubMed corpus language model

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Pre2Pub-Tracking the Path from Preprint to Journal Article: Algorithm Development and Validation

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JOURNAL OF MEDICAL INTERNET RESEARCH 2022年第4期24卷 e34072页

作者： Langnickel, Lisa Podorskaja, Dana Fluck, Juliano ZB Med Informat Ctr Life Sci Gleueler Str 60 D-50931 Cologne Germany Bielefeld Univ Fac Technol Grad Sch Digital Infrastruct Life Sci Bielefeld Inst Bioinformat Infrastruct Bielefeld Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol Bonn Germany Univ Bonn Agr Fac Bonn Germany

Background: The current COVID-19 crisis underscores the importance of preprints, as they allow for rapid communication of research results without delay in review. To fully integrate this type of publication into library information systems, we developed preview: a publicly available, central search engine for COVID-19-related preprints, which clearly distinguishes this source from peer-reviewed publications. The relationship between the preprint version and its corresponding journal version should be stored as metadata in both versions so that duplicates can be easily identified and information overload for researchers is reduced. Objective: In this work, we investigated the extent to which the relationship information between preprint and corresponding journal publication is present in the published metadata, how it can be further completed, and how it can be used in preVIEW to identify already republished preprints and filter those duplicates in search results. Methods: We first analyzed the information content available at the preprint servers themselves and the information that can be retrieved via Crossref. Moreover, we developed the algorithm Pre2Pub to find the corresponding reviewed article for each preprint. We integrated the results of those different resources into our search engine preVIEW, presented the information in the result set overview, and added filter options accordingly. Results: Preprints have found their place in publication workflows;however, the link from a preprint to its corresponding journal publication is not completely covered in the metadata of the preprint servers or in Crossref. Our algorithm Pre2Pub is able to find approximately 16% more related journal articles with a precision of 99.27%. We also integrate this information in a transparent way within preVIEW so that researchers can use it in their search. Conclusions: Relationships between the preprint version and its journal version is valuable information that can help research

关键词： preprints information retrieval COVID-19 metadata BERT bidirectional encoder representations from transformers

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Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study

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JMIR MEDICAL INFORMATICS 2023年 11卷 e46348页

作者： Weng, Kung-Hsun Liu, Chung-Feng Chen, Chia-Jung Chi Mei Med Ctr Dept Med Imaging Tainan Taiwan Chi Mei Med Ctr Dept Med Res Tainan Taiwan Chi Mei Med Ctr Dept Informat Syst Tainan Taiwan Chi Mei Med Ctr Dept Informat Syst 901Zhonghua Rd Tainan 71004 Taiwan

Background: Negation and speculation unrelated to abnormal findings can lead to false-positive alarms for automatic radiology report highlighting or flagging by laboratory information systems. Objective: This internal validation study evaluated the performance of natural language processing methods (NegEx, NegBio, NegBERT, and transformers). Methods: We annotated all negative and speculative statements unrelated to abnormal findings in reports. In experiment 1, we fine-tuned several transformer models (ALBERT [A Lite bidirectional encoder representations from transformers], BERT [bidirectional encoder representations from transformers], DeBERTa [Decoding-Enhanced BERT With Disentangled Attention], DistilBERT [Distilled version of BERT], ELECTRA [Efficiently Learning an encoder That Classifies Token Replacements Accurately], ERNIE [Enhanced Representation through Knowledge Integration], RoBERTa [Robustly Optimized BERT Pretraining Approach], SpanBERT, and XLNet) and compared their performance using precision, recall, accuracy, and F1-scores. In experiment 2, we compared the best model from experiment 1 with 3 established negation and speculation-detection algorithms (NegEx, NegBio, and NegBERT). Results: Our study collected 6000 radiology reports from 3 branches of the Chi Mei Hospital, covering multiple imaging modalities and body parts. A total of 15.01% (105,755/704,512) of words and 39.45% (4529/11,480) of important diagnostic keywords occurred in negative or speculative statements unrelated to abnormal findings. In experiment 1, all models achieved an accuracy of >0.98 and F1-score of >0.90 on the test data set. ALBERT exhibited the best performance (accuracy=0.991;F1-score=0.958). In experiment 2, ALBERT outperformed the optimized NegEx, NegBio, and NegBERT methods in terms of overall performance (accuracy=0.996;F1-score=0.991), in the prediction of whether diagnostic keywords occur in speculative statements unrelated to abnormal findings, and in the improvemen

关键词： radiology report natural language processing negation deep learning transfer learning supervised learning validation study bidirectional encoder representations from transformers BERT clinical application radiology

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Predicting Postoperative Mortality With Deep Neural Networks and Natural Language Processing: Model Development and Validation

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JMIR MEDICAL INFORMATICS 2022年第5期10卷 e38241页

作者： Chen, Pei-Fu Chen, Lichin Lin, Yow-Kuan Li, Guo-Hung Lai, Feipei Lu, Cheng-Wei Yang, Chi-Yu Chen, Kuan-Chih Lin, Tzu-Yu Natl Taiwan Univ Grad Inst Biomed Elect & Bioinformat Taipei Taiwan Far Eastern Mem Hosp Dept Anesthesiol 21Sec 2Nanya S Rd New Taipei 220216 Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan Columbia Univ Dept Comp Sci New York NY 10027 USA Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan Yuan Ze Univ Dept Mech Engn Taoyuan Taiwan Far Eastern Mem Hosp Dept Informat Technol New Taipei Taiwan Far Eastern Mem Hosp Cardiovasc Ctr Sect Cardiovasc Med New Taipei Taiwan Far Eastern Mem Hosp Dept Internal Med New Taipei Taiwan

Background: Machine learning (ML) achieves better predictions of postoperative mortality than previous prediction tools. Free-text descriptions of the preoperative diagnosis and the planned procedure are available preoperatively. Because reading these descriptions helps anesthesiologists evaluate the risk of the surgery, we hypothesized that deep learning (DL) models with unstructured text could improve postoperative mortality prediction. However, it is challenging to extract meaningful concept embeddings from this unstructured clinical text. Objective: This study aims to develop a fusion DL model containing structured and unstructured features to predict the in-hospital 30-day postoperative mortality before surgery. ML models for predicting postoperative mortality using preoperative data with or without free clinical text were assessed. Methods: We retrospectively collected preoperative anesthesia assessments, surgical information, and discharge summaries of patients undergoing general and neuraxial anesthesia from electronic health records (EHRs) from 2016 to 2020. We first compared the deep neural network (DNN) with other models using the same input features to demonstrate effectiveness. Then, we combined the DNN model with bidirectional encoder representations from transformers (BERT) to extract information from clinical texts. The effects of adding text information on the model performance were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Statistical significance was evaluated using P<.05. Results: The final cohort contained 121,313 patients who underwent surgeries. A total of 1562 (1.29%) patients died within 30 days of surgery. Our BERT-DNN model achieved the highest AUROC (0.964, 95% CI 0.961-0.967) and AUPRC (0.336, 95% CI 0.276-0.402). The AUROC of the BERT-DNN was significantly higher compared to logistic regression (AUROC=0.952, 95% CI 0.949-0.955) and the America

关键词： bidirectional encoder representations from transformers deep neural network natural language processing postoperative mortality prediction unstructured text machine learning preoperative medicine anesthesia prediction model anesthesiologist deep learning model electronic health record neural network

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When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2022年第SUPPL 9期21卷 377-377页

作者： Li, Xuedong Yuan, Walter Peng, Dezhong Mei, Qiaozhu Wang, Yue Sichuan Univ Coll Comp Sci Chengdu Peoples R China MobLab Inc Pasadena CA USA Univ Michigan Sch Informat Ann Arbor MI 48109 USA Univ N Carolina Sch Informat & Lib Sci Chapel Hill NC 27515 USA

Background: Natural language processing (NLP) tasks in the health domain often deal with limited amount of labeled data due to high annotation costs and naturally rare observations. To compensate for the lack of training data, health NLP researchers often have to leverage knowledge and resources external to a task at hand. Recently, pretrained large-scale language models such as the bidirectional encoder representations from transformers (BERT) have been proven to be a powerful way of learning rich linguistic knowledge from massive unlabeled text and transferring that knowledge to downstream tasks. However, previous downstream tasks often used training data at such a large scale that is unlikely to obtain in the health domain. In this work, we aim to study whether BERT can still benefit downstream tasks when training data are relatively small in the context of health NLP. Method: We conducted a learning curve analysis to study the behavior of BERT and baseline models as training data size increases. We observed the classification performance of these models on two disease diagnosis data sets, where some diseases are naturally rare and have very limited observations (fewer than 2 out of 10,000). The baselines included commonly used text classification models such as sparse and dense bag-of-words models, long short-term memory networks, and their variants that leveraged external knowledge. To obtain learning curves, we incremented the amount of training examples per disease from small to large, and measured the classification performance in macro-averaged F-1 score. Results: On the task of classifying all diseases, the learning curves of BERT were consistently above all baselines, significantly outperforming them across the spectrum of training data sizes. But under extreme situations where only one or two training documents per disease were available, BERT was outperformed by linear classifiers with carefully engineered bag-of-words features. Conclusion: As long as t

关键词： Learning curve bidirectional encoder representations from transformers Disease classification

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Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach (vol 25, e45767, 2023)

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JOURNAL OF MEDICAL INTERNET RESEARCH 2023年第1期25卷 e45767页

作者： Dolatabadi, Elham Moyano, Diana Bales, Michael Spasojevic, Sofija Bhambhoria, Rohan Bhatti, Junaid Debnath, Shyamolima Hoell, Nicholas Li, Xin Leng, Celine Nanda, Sasha Saab, Jad Sahak, Esmat Sie, Fanny Uppal, Sara Vadlamudi, Nirma Khatri Vladimirova, Antoaneta Yakimovich, Artur Yang, Xiaoxue Kocak, Sedef Akinli Cheung, Angela M. York Univ Fac Hlth Sch Hlth Policy & Management Toronto ON Canada Vector Inst Toronto ON Canada Univ Toronto Dept Med & Joint Dept Med Imaging Toronto ON Canada Hoffmann La Roche Ltd Mississauga ON Canada Queens Univ Elect & Comp Engn Kingston ON Canada Manulife Canada Toronto ON Canada Deloitte Toronto ON Canada TELUS Hlth Montreal PQ Canada Univ British Columbia Fac Med Dept Pediat Vancouver BC Canada Roche Informat Solut San Francisco CA USA Hoffmann La Roche Ltd Munich Germany Univ Hlth Network Toronto ON Canada York Univ Sch Hlth Policy & Management Fac Hlth 4700 Keele St Toronto ON M3J 1P3 Canada

BACKGROUNDWhile scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most *** this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance *** used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several ***-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuro

关键词： long COVID post–COVID-19 condition PCC social media natural language processing transformer models bidirectional encoder representations from transformers machine learning Twitter Reddit PRO patient-reported outcome patient-reported symptom health outcome symptom entity extraction entity normalization

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