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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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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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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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Semantic Textual Similarity in Japanese Clinical Domain Texts Using BERT

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METHODS OF INFORMATION IN MEDICINE 2021年第S 01期60卷 E56-E64页

作者： Mutinda, Faith Wavinya Yada, Shuntaro Wakamiya, Shoko Aramaki, Eiji Nara Inst Sci & Technol Grad Sch Sci & Technol 8916-5 Takayama Cho Nara 6300192 Japan

Background Semantic textual similarity (STS) captures the degree of semantic similarity between texts. It plays an important role in many natural language processing applications such as text summarization, question answering, machine translation, information retrieval, dialog systems, plagiarism detection, and query ranking. STS has been widely studied in the general English domain. However, there exists few resources for STS tasks in the clinical domain and in languages other than English, such as Japanese. Objective The objective of this study is to capture semantic similarity between Japanese clinical texts (Japanese clinical STS) by creating a Japanese dataset that is publicly available. Materials We created two datasets for Japanese clinical STS: (1) Japanese case reports (CR dataset) and (2) Japanese electronic medical records (EMR dataset). The CR dataset was created from publicly available case reports extracted from the CiNii database. The EMR dataset was created from Japanese electronic medical records. Methods We used an approach based on bidirectional encoder representations from transformers (BERT) to capture the semantic similarity between the clinical domain texts. BERT is a popular approach for transfer learning and has been proven to be effective in achieving high accuracy for small datasets. We implemented two Japanese pretrained BERT models: a general Japanese BERT and a clinical Japanese BERT. The general Japanese BERT is pretrained on Japanese Wikipedia texts while the clinical Japanese BERT is pretrained on Japanese clinical texts. Results The BERT models performed well in capturing semantic similarity in our datasets. The general Japanese BERT outperformed the clinical Japanese BERT and achieved a high correlation with human score (0.904 in the CR dataset and 0.875 in the EMR dataset). It was unexpected that the general Japanese BERT outperformed the clinical Japanese BERT on clinical domain dataset. This could be due to the fact that the gen

关键词： natural language processing semantic textual similarity clinical semantic textual similarity 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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BERT-Based Hybrid RNN Model for Multi-class Text Classification to Study the Effect of Pre-trained Word Embeddings

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第9期13卷 667-674页

作者： Shreyashree, S. Sunagar, Pramod Rajarajeswari, S. Kanavalli, Anita VTU MS Ramaiah Inst Technol Dept Comp Sci & Engn Bengaluru India

Due to the Covid-19 pandemic which started in the year 2020, many nations had imposed lockdown to curb the spread of this virus. People have been sharing their experiences and perspectives on social media on the lockdown situation. This has given rise to increased number of tweets or posts on social media. Multi-class text classification, a method of classifying a text into one of the pre-defined categories, is one of the effective ways to analyze such data that is implemented in this paper. A Covid-19 dataset is used in this work consisting of fifteen pre-defined categories. This paper presents a multi-layered hybrid model, LSTM followed by GRU, to integrate the benefits of both the techniques. The advantages of word embeddings techniques like GloVe and BERT have been implemented and found that, for three epochs, the transfer learning based pre-trained BERT-hybrid model performs one percent better than GloVe-hybrid model but the state-of-the-art, fine-tuned BERT-base model outperforms the BERT-hybrid model by three percent, in terms of validation loss. It is expected that, over a larger number of epochs, the hybrid model might outperform the fine-tuned model.

关键词： Multi-class text classification transfer learning pre-training word embeddings GloVe bidirectional encoder representations from transformers long short-term memory gated recurrent units hybrid model RNN

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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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A Review-based Context-Aware Recommender Systems: Using Custom NER and Factorization Machines

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第3期13卷 546-553页

作者： Madani, Rabie Ez-zahout, Abderrahmane Mohammed V Univ Fac Sci Comp Sci Dept Intelligent Proc & Secur Syst Team Rabat Morocco

Recommender Systems depend fundamentally on user feedback to provide recommendation. Classical Recommenders are based only on historical data and also suffer from several problems linked to the lack of data such as sparsity. Users' reviews represent a massive amount of valuable and rich knowledge information, but they are still ignored by most of current recommender systems. Information such as users' preferences and contextual data could be extracted from reviews and integrated into Recommender Systems to provide more accurate recommendations. In this paper, we present a Context Aware Recommender System model, based on a bidirectional encoder representations from transformers (BERT) pretrained model to customize Named Entity Recognition (NER). The model allows to automatically extract contextual information from reviews then insert extracted data into a Contextual Machine Factorization to compte and predict ratings. Empirical results show that our model improves the quality of recommendation and outperforms existing Recommender Systems.

关键词： Recommender systems context aware recommender systems factorization machines bidirectional encoder representations from transformers named entity recognition

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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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