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28th International Conference on Neural Information Processing

作者： Dhiaf, Marwa Jemni, Sana Khamekhem Kessentini, Yousri Digital Res Ctr Sfax SMRTS Lab Signals Syst aRtificial Intelligence & Sfax Tunisia Digital Res Ctr Sfax MIR CL Multimedia InfoRmat Syst & Adv Comp Lab Sfax Tunisia

ISBN: (纸本)9783030923099;9783030923105

The extraction of relevant information from handwritten document images is still a challenging task. In this paper, we introduce a framework for named entities recognition from a collection of real handwritten marriage records. For this purpose, we perform an extensive evaluation of two different information extraction approaches to locate and recognize named entities from unstructured handwritten documents. The first one is based on an end-to-end neural network model that jointly performs transcription and semantic annotation of handwritten document images. While the second relies on two stages, the first one focuses on Handwritten Text Recognition (HTR) to transcribe documents into electronic texts, while the second seeks to identify semantic named entities using a state-of-the-art Natural Language Processing (NLP) model. This work is evaluated on a collection of real French handwritten marriage records, discussing the advantages and drawbacks of the explored approaches. The achieved results show the efficiency of the proposed framework even though it does not use any post-processing stage. Additional experiments are conducted using the Esposalles database to compare our methods to the participated systems in the ICDAR 2017 Information Extraction competition.

关键词： Handwriting text recognition Named entity recognition bidirectional Long Short Term Memory network bidirectional encoder representations from transformers

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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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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 Most Effective Interventions for Classification ModelDevelopment to Predict Chat Outcomes Based on theConversation Content in Online Suicide Prevention Chats:MachineLearning Approach

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JMIR MENTAL HEALTH 2024年 11卷 e57362页

作者： Salmi, Salim Merel, Saskia Gilisse, Renske van der Mei, Rob Bhulai, Sandjai 113 Suicide Prevent Res Dept Paasheuvelweg 25 NL-1105 BP Amsterdam Netherlands Ctr Wiskunde & Informat Dept Stochast Amsterdam Netherlands Vrije Univ Amsterdam Dept Math Amsterdam Netherlands

Background: For the provision of optimal care in a suicide prevention helpline, it is important to know what contributes topositive or negative effects on help seekers. Helplines can often be contacted through text-based chat services, which producelarge amounts of text data for use in large-scale analysis. Objective: We trained a machine learning classification model to predict chat outcomes based on the content of the chatconversations in suicide helplines and identified the counsellor utterances that had the most impact on its outputs. Methods: from August 2021 until January 2023, help seekers (N=6903) scored themselves on factors known to be associatedwith suicidality (eg, hopelessness, feeling entrapped, will to live) before and after a chat conversation with the suicide preventionhelpline in the Netherlands (113 Suicide Prevention). Machine learning text analysis was used to predict help seeker scores onthese factors. Using 2 approaches for interpreting machine learning models, we identified text messages from helpers in a chatthat contributed the most to the prediction of the model. Results: According to the machine learning model, helpers'positive affirmations and expressing involvement contributed toimproved scores of the help seekers. Use of macros and ending the chat prematurely due to the help seeker being in an unsafesituation had negative effects on help seekers. Conclusions: This study reveals insights for improving helpline chats, emphasizing the value of an evocative style with questions,positive affirmations, and practical advice. It also underscores the potential of machine learning in helpline chat analysi

关键词： suicide suicidality suicide prevention helpline suicide helpline classification interpretable AI explainable AI conversations BERT bidirectional encoder representations from transformers machine learning artificial intelligence large language models LLM natural language processing

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Large Language Models for Mental Health Applications:Systematic Review

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JMIR MENTAL HEALTH 2024年 11卷 e57400页

作者： Guo, Zhijun Lai, Alvina Thygesen, Johan H. Farrington, Joseph Keen, Thomas Li, Kezhi UCL Inst Hlth Informat 222 Euston Rd London NW1 2DA England UCL Great Ormond St Inst Child Hlth London England

Background: Large language models (LLMs) are advanced artificial neural networks trained on extensive datasets to accurately understand and generate natural language. While they have received much attention and demonstrated potential in digital health, their application in mental health, particularly in clinical settings, has generated considerable debate. Objective: This systematic review aims to critically assess the use of LLMs in mental health, specifically focusing on theirapplicability and efficacy in early screening, digital interventions, and clinical settings. By systematically collating and assessingthe evidence from current studies, our work analyzes models, methodologies, data sources, and outcomes, thereby highlightingthe potential of LLMs in mental health, the challenges they present, and the prospects for their clinical use. Methods: Adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, thisreview searched 5 open-access databases: MEDLINE (accessed by PubMed), IEEE Xplore, Scopus, JMIR, and ACM DigitalLibrary. Keywords used were (mental healthOR mental illnessOR mental disorderOR psychiatry) AND (large languagemodels). This study included articles published between January 1, 2017, and April 30, 2024, and excluded articles published inlanguages other than English. Results: In total, 40 articles were evaluated, including 15 (38%) articles on mental health conditions and suicidal ideationdetection through text analysis, 7 (18%) on the use of LLMs as mental health conversational agents, and 18 (45%) on otherapplications and evaluations of LLMs in mental health. LLMs show good effectiveness in detecting mental health issues andproviding accessible, destigmatized eHealth services. However, assessments also indicate that the current risks associated withclinical use might surpass their benefits. These risks include inconsistencies in generated text;the production of hallucinations;and the absence of a compre

关键词： large language models mental health digital health care ChatGPT bidirectional encoder representations from transformers BERT

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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 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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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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Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

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JOURNAL OF MEDICAL INTERNET RESEARCH 2023年第1期25卷 e47014页

作者： Laison, Elda Kokoe Elolo Ibrahim, Mohamed Hamza Boligarla, Srikanth Li, Jiaxin Mahadevan, Raja Ng, Austen Muthuramalingam, Venkataraman Lee, Wee Yi Yin, Yijun Nasri, Bouchra R. Univ Montreal Ecole St Publ Univ Montreal Dept Med Sociale & Prevent Montreal PQ Canada Zagazig Univ Fac Sci Dept Math Zagazig Egypt Harvard Univ Harvard Extens Sch Cambridge MA 02138 USA Univ Montreal Sch Publ Hlth Dept Social & Prevent Med 7101 Pk Ave Montreal PQ H3N 1X9 Canada

Background: Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals, and accurate laboratory testing and interpretation for clinical diagnosis validation. A lack of these can lead to delayed diagnosis and treatment, which can exacerbate the severity of Lyme disease symptoms. Therefore, there is a need to improve the monitoring of Lyme disease by using other data sources, such as web-based data. Objective: We analyzed global Twitter data to understand its potential and limitations as a tool for Lyme disease surveillance. We propose a transformer-based classification system to identify potential Lyme disease cases using self-reported tweets. Methods: Our initial sample included 20,000 tweets collected worldwide from a database of over 1.3 million Lyme disease tweets. After preprocessing and geolocating tweets, tweets in a subset of the initial sample were manually labeled as potential Lyme disease cases or non-Lyme disease cases using carefully selected keywords. Emojis were converted to sentiment words, which were then replaced in the tweets. This labeled tweet set was used for the training, validation, and performance testing of DistilBERT (distilled version of BERT [bidirectional encoder representations from transformers]), ALBERT (A Lite BERT), and BERTweet (BERT for English Tweets) classifiers. Results: The empirical results showed that BERTweet was the best classifier among all evaluated models (average F1-score of 89.3%, classification accuracy of 90.0%, and precision of 97.1%). However, for recall, term frequency-inverse document frequency and k-nearest neighbors performed better (93.2% and 82.6%, respectively). On using emojis to enrich the tweet embeddings, BERTweet had an increased recall (8% increase), DistilBERT had an increased F1-score of 93.8% (4% increase) and classificat

关键词： Lyme disease Twitter BERT bidirectional encoder representations from transformers emojis machine learning natural language processing

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Medical text classification based on the discriminative pre-training model and prompt-tuning

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DIGITAL HEALTH 2023年 9卷

作者： Wang, Yu Wang, Yuan Peng, Zhenwan Zhang, Feifan Zhou, Luyao Yang, Fei Anhui Med Univ Sch Biomed Engn Hefei Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei Peoples R China

Medical text classification, as a fundamental medical natural language processing task, aims to identify the categories to which a short medical text belongs. Current research has focused on performing the medical text classification task using a pre-training language model through fine-tuning. However, this paradigm introduces additional parameters when training extra classifiers. Recent studies have shown that the "prompt-tuning" paradigm induces better performance in many natural language processing tasks because it bridges the gap between pre-training goals and downstream tasks. The main idea of prompt-tuning is to transform binary or multi-classification tasks into mask prediction tasks by fully exploiting the features learned by pre-training language models. This study explores, for the first time, how to classify medical texts using a discriminative pre-training language model called ERNIE-Health through prompt-tuning. Specifically, we attempt to perform prompt-tuning based on the multi-token selection task, which is a pre-training task of ERNIE-Health. The raw text is wrapped into a new sequence with a template in which the category label is replaced by a [UNK] token. The model is then trained to calculate the probability distribution of the candidate categories. Our method is tested on the KUAKE-Question Intention Classification and CHiP-Clinical Trial Criterion datasets and obtains the accuracy values of 0.866 and 0.861. In addition, the loss values of our model decrease faster throughout the training period compared to the fine-tuning. The experimental results provide valuable insights to the community and suggest that prompt-tuning can be a promising approach to improve the performance of pre-training models in domain-specific tasks.

关键词： Text classification pre-training language model prompt-tuning ERNIE-Health bidirectional encoder representations from transformers

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