发布求助
文献互助 智能选刊 最新文献

Clinical Natural Language Processing Workshop最新文献

筛选
英文 中文
Medical Visual Textual Entailment for Numerical Understanding of Vision-and-Language Models 医学视觉文本蕴涵对视觉和语言模型的数值理解
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.2
Hitomi Yanaka, Yuta Nakamura, Yuki Chida, Tomoya Kurosawa
{"title":"Medical Visual Textual Entailment for Numerical Understanding of Vision-and-Language Models","authors":"Hitomi Yanaka, Yuta Nakamura, Yuki Chida, Tomoya Kurosawa","doi":"10.18653/v1/2023.clinicalnlp-1.2","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.2","url":null,"abstract":"Assessing the capacity of numerical understanding of vision-and-language models over images and texts is crucial for real vision-and-language applications, such as systems for automated medical image analysis.We provide a visual reasoning dataset focusing on numerical understanding in the medical domain.The experiments using our dataset show that current vision-and-language models fail to perform numerical inference in the medical domain.However, the data augmentation with only a small amount of our dataset improves the model performance, while maintaining the performance in the general domain.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"28 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114028147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Uncovering the Potential for a Weakly Supervised End-to-End Model in Recognising Speech from Patient with Post-Stroke Aphasia 揭示弱监督端到端模型在卒中后失语症患者语音识别中的潜力
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.24
Giulia Sanguedolce, P. Naylor, F. Geranmayeh
{"title":"Uncovering the Potential for a Weakly Supervised End-to-End Model in Recognising Speech from Patient with Post-Stroke Aphasia","authors":"Giulia Sanguedolce, P. Naylor, F. Geranmayeh","doi":"10.18653/v1/2023.clinicalnlp-1.24","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.24","url":null,"abstract":"Post-stroke speech and language deficits (aphasia) significantly impact patients’ quality of life. Many with mild symptoms remain undiagnosed, and the majority do not receive the intensive doses of therapy recommended, due to healthcare costs and/or inadequate services. Automatic Speech Recognition (ASR) may help overcome these difficulties by improving diagnostic rates and providing feedback during tailored therapy. However, its performance is often unsatisfactory due to the high variability in speech errors and scarcity of training datasets. This study assessed the performance of Whisper, a recently released end-to-end model, in patients with post-stroke aphasia (PWA). We tuned its hyperparameters to achieve the lowest word error rate (WER) on aphasic speech. WER was significantly higher in PWA compared to age-matched controls (10.3% vs 38.5%, p<0.001). We demonstrated that worse WER was related to the more severe aphasia as measured by expressive (overt naming, and spontaneous speech production) and receptive (written and spoken comprehension) language assessments. Stroke lesion size did not affect the performance of Whisper. Linear mixed models accounting for demographic factors, therapy duration, and time since stroke, confirmed worse Whisper performance with left hemispheric frontal lesions.We discuss the implications of these findings for how future ASR can be improved in PWA.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130922579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Method for Designing Semantic Annotation of Sepsis Signs in Clinical Text 脓毒症临床文本语义标注设计方法
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.29
Melissa Y. Yan, L. Gustad, L. Høvik, Ø. Nytrø
{"title":"Method for Designing Semantic Annotation of Sepsis Signs in Clinical Text","authors":"Melissa Y. Yan, L. Gustad, L. Høvik, Ø. Nytrø","doi":"10.18653/v1/2023.clinicalnlp-1.29","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.29","url":null,"abstract":"Annotated clinical text corpora are essential for machine learning studies that model and predict care processes and disease progression. However, few studies describe the necessary experimental design of the annotation guideline and annotation phases. This makes replication, reuse, and adoption challenging.Using clinical questions about sepsis, we designed a semantic annotation guideline to capture sepsis signs from clinical text. The clinical questions aid guideline design, application, and evaluation. Our method incrementally evaluates each change in the guideline by testing the resulting annotated corpus using clinical questions. Additionally, our method uses inter-annotator agreement to judge the annotator compliance and quality of the guideline. We show that the method, combined with controlled design increments, is simple and allows the development and measurable improvement of a purpose-built semantic annotation guideline. We believe that our approach is useful for incremental design of semantic annotation guidelines in general.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130031889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Tracking the Evolution of Covid-19 Symptoms through Clinical Conversations 通过临床对话追踪Covid-19症状演变
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.6
T. C. D. Silva, J. Macêdo, R. P. Magalhães
{"title":"Tracking the Evolution of Covid-19 Symptoms through Clinical Conversations","authors":"T. C. D. Silva, J. Macêdo, R. P. Magalhães","doi":"10.18653/v1/2023.clinicalnlp-1.6","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.6","url":null,"abstract":"The Coronavirus pandemic has heightened the demand for technological solutions capable of gathering and monitoring data automatically, quickly, and securely. To achieve this need, the Plantão Coronavirus chatbot has been made available to the population of Ceará State in Brazil. This chatbot employs automated symptom detection technology through Natural Language Processing (NLP). The proposal of this work is a symptom tracker, which is a neural network that processes texts and captures symptoms in messages exchanged between citizens of the state and the Plantão Coronavirus nurse/doctor, i.e., clinical conversations. The model has the ability to recognize new patterns and has identified a high incidence of altered psychological behaviors, including anguish, anxiety, and sadness, among users who tested positive or negative for Covid-19. As a result, the tool has emphasized the importance of expanding coverage through community mental health services in the state.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127741533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Who needs context? Classical techniques for Alzheimer’s disease detection 谁需要背景?阿尔茨海默病检测的经典技术
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.13
Behrad TaghiBeyglou, Frank Rudzicz
{"title":"Who needs context? Classical techniques for Alzheimer’s disease detection","authors":"Behrad TaghiBeyglou, Frank Rudzicz","doi":"10.18653/v1/2023.clinicalnlp-1.13","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.13","url":null,"abstract":"Natural language processing (NLP) has shown great potential for Alzheimer’s disease (AD) detection, particularly due to the adverse effect of AD on spontaneous speech. The current body of literature has directed attention toward context-based models, especially Bidirectional Encoder Representations from Transformers (BERTs), owing to their exceptional abilities to integrate contextual information in a wide range of NLP tasks.This comes at the cost of added model opacity and computational requirements. Taking this into consideration, we propose a Word2Vec-based model for AD detection in 108 age- and sex-matched participants who were asked to describe the Cookie Theft picture. We also investigate the effectiveness of our model by fine-tuning BERT-based sequence classification models, as well as incorporating linguistic features. Our results demonstrate that our lightweight and easy-to-implement model outperforms some of the state-of-the-art models available in the literature, as well as BERT models.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127443642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Can Large Language Models Safely Address Patient Questions Following Cataract Surgery? 大型语言模型能安全地解决白内障手术后患者的问题吗?
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.17
Mohita Chowdhury, Ernest Lim, A. Higham, R. McKinnon, Nikoletta Ventoura, Yajie He, N. D. Pennington
{"title":"Can Large Language Models Safely Address Patient Questions Following Cataract Surgery?","authors":"Mohita Chowdhury, Ernest Lim, A. Higham, R. McKinnon, Nikoletta Ventoura, Yajie He, N. D. Pennington","doi":"10.18653/v1/2023.clinicalnlp-1.17","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.17","url":null,"abstract":"Recent advances in large language models (LLMs) have generated significant interest in their application across various domains including healthcare. However, there is limited data on their safety and performance in real-world scenarios. This study uses data collected using an autonomous telemedicine clinical assistant. The assistant asks symptom-based questions to elicit patient concerns and allows patients to ask questions about their post-operative recovery. We utilise real-world postoperative questions posed to the assistant by a cohort of 120 patients to examine the safety and appropriateness of responses generated by a recent popular LLM by OpenAI, ChatGPT. We demonstrate that LLMs have the potential to helpfully address routine patient queries following routine surgery. However, important limitations around the safety of today’s models exist which must be considered.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"469 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131025082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Overview of the MEDIQA-Chat 2023 Shared Tasks on the Summarization & Generation of Doctor-Patient Conversations MEDIQA-Chat 2023关于医患对话总结和生成的共享任务概述
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.52
Asma Ben Abacha, Wen-wai Yim, Griffin Adams, N. Snider, Meliha Yetisgen-Yildiz
{"title":"Overview of the MEDIQA-Chat 2023 Shared Tasks on the Summarization & Generation of Doctor-Patient Conversations","authors":"Asma Ben Abacha, Wen-wai Yim, Griffin Adams, N. Snider, Meliha Yetisgen-Yildiz","doi":"10.18653/v1/2023.clinicalnlp-1.52","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.52","url":null,"abstract":"Automatic generation of clinical notes from doctor-patient conversations can play a key role in reducing daily doctors’ workload and improving their interactions with the patients. MEDIQA-Chat 2023 aims to advance and promote research on effective solutions through shared tasks on the automatic summarization of doctor-patient conversations and on the generation of synthetic dialogues from clinical notes for data augmentation. Seventeen teams participated in the challenge and experimented with a broad range of approaches and models. In this paper, we describe the three MEDIQA-Chat 2023 tasks, the datasets, and the participants’ results and methods. We hope that these shared tasks will lead to additional research efforts and insights on the automatic generation and evaluation of clinical notes.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132205422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
Leveraging Natural Language Processing and Clinical Notes for Dementia Detection 利用自然语言处理和临床记录来检测痴呆症
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.20
Ming Liu, R. Beare, T. Collyer, Nadine E. Andrew, V. Srikanth
{"title":"Leveraging Natural Language Processing and Clinical Notes for Dementia Detection","authors":"Ming Liu, R. Beare, T. Collyer, Nadine E. Andrew, V. Srikanth","doi":"10.18653/v1/2023.clinicalnlp-1.20","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.20","url":null,"abstract":"Early detection and automated classification of dementia has recently gained considerable attention using neuroimaging data and spontaneous speech. In this paper, we validate the possibility of dementia detection with in-hospital clinical notes. We collected 954 patients’ clinical notes from a local hospital and assign dementia/non-dementia labels to those patients based on clinical assessment and telephone interview. Given the labeled dementia data sets, we fine tune a ClinicalBioBERT based on some filtered clinical notes and conducted experiments on both binary and three class dementia classification. Our experiment results show that the fine tuned ClinicalBioBERT achieved satisfied performance on binary classification but failed on three class dementia classification. Further analysis suggests that more human prior knowledge should be considered.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124198759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
NewAgeHealthWarriors at MEDIQA-Chat 2023 Task A: Summarizing Short Medical Conversation with Transformers NewAgeHealthWarriors在MEDIQA-Chat 2023任务A:总结与变形金刚的简短医疗对话
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.44
Prakhar Mishra, Ravi Theja Desetty
{"title":"NewAgeHealthWarriors at MEDIQA-Chat 2023 Task A: Summarizing Short Medical Conversation with Transformers","authors":"Prakhar Mishra, Ravi Theja Desetty","doi":"10.18653/v1/2023.clinicalnlp-1.44","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.44","url":null,"abstract":"This paper presents the MEDIQA-Chat 2023 shared task organized at the ACL-Clinical NLP workshop. The shared task is motivated by the need to develop methods to automatically generate clinical notes from doctor-patient conversations. In this paper, we present our submission for MEDIQA-Chat 2023 Task A: Short Dialogue2Note Summarization. Manual creation of these clinical notes requires extensive human efforts, thus making it a time-consuming and expensive process. To address this, we propose an ensemble-based method over GPT-3, BART, BERT variants, and Rule-based systems to automatically generate clinical notes from these conversations. The proposed system achieves a score of 0.730 and 0.544 for both the sub-tasks on the test set (ranking 8th on the leaderboard for both tasks) and shows better performance compared to a baseline system using BART variants.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127992378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Navigating Data Scarcity: Pretraining for Medical Utterance Classification 导航数据稀缺:医学话语分类的预训练
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.8
Do June Min, Verónica Pérez-Rosas, Rada Mihalcea
{"title":"Navigating Data Scarcity: Pretraining for Medical Utterance Classification","authors":"Do June Min, Verónica Pérez-Rosas, Rada Mihalcea","doi":"10.18653/v1/2023.clinicalnlp-1.8","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.8","url":null,"abstract":"Pretrained language models leverage self-supervised learning to use large amounts of unlabeled text for learning contextual representations of sequences. However, in the domain of medical conversations, the availability of large, public datasets is limited due to issues of privacy and data management. In this paper, we study the effectiveness of dialog-aware pretraining objectives and multiphase training in using unlabeled data to improve LMs training for medical utterance classification. The objectives of pretraining for dialog awareness involve tasks that take into account the structure of conversations, including features such as turn-taking and the roles of speakers. The multiphase training process uses unannotated data in a sequence that prioritizes similarities and connections between different domains. We empirically evaluate these methods on conversational dialog classification tasks in the medical and counseling domains, and find that multiphase training can help achieve higher performance than standard pretraining or finetuning.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116776260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信