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
Privacy-Preserving Knowledge Transfer through Partial Parameter Sharing 基于部分参数共享的保护隐私的知识传递
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.3
Paul Youssef, Jörg Schlötterer, C. Seifert
{"title":"Privacy-Preserving Knowledge Transfer through Partial Parameter Sharing","authors":"Paul Youssef, Jörg Schlötterer, C. Seifert","doi":"10.18653/v1/2023.clinicalnlp-1.3","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.3","url":null,"abstract":"Valuable datasets that contain sensitive information are not shared due to privacy and copyright concerns. This hinders progress in many areas and prevents the use of machine learning solutions to solve relevant tasks. One possible solution is sharing models that are trained on such datasets. However, this is also associated with potential privacy risks due to data extraction attacks. In this work, we propose a solution based on sharing parts of the model’s parameters, and using a proxy dataset for complimentary knowledge transfer. Our experiments show encouraging results, and reduced risk to potential training data identification attacks. We present a viable solution to sharing knowledge with data-disadvantaged parties, that do not have the resources to produce high-quality data, with reduced privacy risks to the sharing parties. We make our code publicly available.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"44 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":"133734355","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
Cross-domain German Medical Named Entity Recognition using a Pre-Trained Language Model and Unified Medical Semantic Types 基于预训练语言模型和统一医学语义类型的跨领域德语医学命名实体识别
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.31
Siting Liang, Mareike Hartmann, Daniel Sonntag
{"title":"Cross-domain German Medical Named Entity Recognition using a Pre-Trained Language Model and Unified Medical Semantic Types","authors":"Siting Liang, Mareike Hartmann, Daniel Sonntag","doi":"10.18653/v1/2023.clinicalnlp-1.31","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.31","url":null,"abstract":"Information extraction from clinical text has the potential to facilitate clinical research and personalized clinical care, but annotating large amounts of data for each set of target tasks is prohibitive. We present a German medical Named Entity Recognition (NER) system capable of cross-domain knowledge transferring. The system builds on a pre-trained German language model and a token-level binary classifier, employing semantic types sourced from the Unified Medical Language System (UMLS) as entity labels to identify corresponding entity spans within the input text. To enhance the system’s performance and robustness, we pre-train it using a medical literature corpus that incorporates UMLS semantic term annotations. We evaluate the system’s effectiveness on two German annotated datasets obtained from different clinics in zero- and few-shot settings. The results show that our approach outperforms task-specific Condition Random Fields (CRF) classifiers in terms of accuracy. Our work contributes to developing robust and transparent German medical NER models that can support the extraction of information from various clinical texts.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"97 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":"126891233","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
Automated Orthodontic Diagnosis from a Summary of Medical Findings 基于医学发现摘要的自动正畸诊断
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.21
Takumi Ohtsuka, Tomoyuki Kajiwara, C. Tanikawa, Yuujin Shimizu, Hajime Nagahara, Takashi Ninomiya
{"title":"Automated Orthodontic Diagnosis from a Summary of Medical Findings","authors":"Takumi Ohtsuka, Tomoyuki Kajiwara, C. Tanikawa, Yuujin Shimizu, Hajime Nagahara, Takashi Ninomiya","doi":"10.18653/v1/2023.clinicalnlp-1.21","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.21","url":null,"abstract":"We propose a method to automate orthodontic diagnosis with natural language processing. It is worthwhile to assist dentists with such technology to prevent errors by inexperienced dentists and to reduce the workload of experienced ones. However, text length and style inconsistencies in medical findings make an automated orthodontic diagnosis with deep-learning models difficult. In this study, we improve the performance of automatic diagnosis utilizing short summaries of medical findings written in a consistent style by experienced dentists. Experimental results on 970 Japanese medical findings show that summarization consistently improves the performance of various machine learning models for automated orthodontic diagnosis. Although BERT is the model that gains the most performance with the proposed method, the convolutional neural network achieved the best performance.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"83 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":"126023662","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
Prompt Discriminative Language Models for Domain Adaptation 领域适应的提示判别语言模型
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.30
K. Lu, P. Potash, Xihui Lin, Yuwen Sun, Zihan Qian, Zheng Yuan, Tristan Naumann, Tianxi Cai, Junwei Lu
{"title":"Prompt Discriminative Language Models for Domain Adaptation","authors":"K. Lu, P. Potash, Xihui Lin, Yuwen Sun, Zihan Qian, Zheng Yuan, Tristan Naumann, Tianxi Cai, Junwei Lu","doi":"10.18653/v1/2023.clinicalnlp-1.30","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.30","url":null,"abstract":"Prompt tuning offers an efficient approach to domain adaptation for pretrained language models, which predominantly focus on masked language modeling or generative objectives. However, the potential of discriminative language models in biomedical tasks remains underexplored.To bridge this gap, we develop BioDLM, a method tailored for biomedical domain adaptation of discriminative language models that incorporates prompt-based continual pretraining and prompt tuning for downstream tasks. BioDLM aims to maximize the potential of discriminative language models in low-resource scenarios by reformulating these tasks as span-level corruption detection, thereby enhancing performance on domain-specific tasks and improving the efficiency of continual pertaining.In this way, BioDLM provides a data-efficient domain adaptation method for discriminative language models, effectively enhancing performance on discriminative tasks within the biomedical domain.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"31 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":"132475989","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
Teddysum at MEDIQA-Chat 2023: an analysis of fine-tuning strategy for long dialog summarization Teddysum在MEDIQA-Chat 2023:长对话摘要的微调策略分析
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.42
Yongbin Jeong, J. Han, Kyung Min Chae, Yousang Cho, Hyun-Kyoung Seo, Kyungtae Lim, Key-sun Choi, YoungGyun Hahm
{"title":"Teddysum at MEDIQA-Chat 2023: an analysis of fine-tuning strategy for long dialog summarization","authors":"Yongbin Jeong, J. Han, Kyung Min Chae, Yousang Cho, Hyun-Kyoung Seo, Kyungtae Lim, Key-sun Choi, YoungGyun Hahm","doi":"10.18653/v1/2023.clinicalnlp-1.42","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.42","url":null,"abstract":"In this paper, we introduce the design and various attempts for TaskB of MEDIQA-Chat 2023. The goal of TaskB in MEDIQA-Chat 2023 is to generate full clinical note from doctor-patient consultation dialogues. This task has several challenging issues, such as lack of training data, handling long dialogue inputs, and generating semi-structured clinical note which have section heads. To address these issues, we conducted various experiments and analyzed their results. We utilized the DialogLED model pre-trained on long dialogue data to handle long inputs, and we pre-trained on other dialogue datasets to address the lack of training data. We also attempted methods such as using prompts and contrastive learning for handling sections. This paper provides insights into clinical note generation through analyzing experimental methods and results, and it suggests future research directions.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"23 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":"124661806","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
Storyline-Centric Detection of Aphasia and Dysarthria in Stroke Patient Transcripts 以故事情节为中心的脑卒中患者记录中的失语和构音障碍检测
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.45
Peiqi Sui, K. Wong, Xiaohui Yu, John Volpi, Stephen T. C. Wong
{"title":"Storyline-Centric Detection of Aphasia and Dysarthria in Stroke Patient Transcripts","authors":"Peiqi Sui, K. Wong, Xiaohui Yu, John Volpi, Stephen T. C. Wong","doi":"10.18653/v1/2023.clinicalnlp-1.45","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.45","url":null,"abstract":"Aphasia and dysarthria are both common symptoms of stroke, affecting around 30% and 50% of acute ischemic stroke patients. In this paper, we propose a storyline-centric approach to detect aphasia and dysarthria in acute stroke patients using transcribed picture descriptions alone. Our pipeline enriches the training set with healthy data to address the lack of acute stroke patient data and utilizes knowledge distillation to significantly improve upon a document classification baseline, achieving an AUC of 0.814 (aphasia) and 0.764 (dysarthria) on a patient-only validation set.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"62 43","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120888828","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
Context-aware Medication Event Extraction from Unstructured Text 从非结构化文本中提取上下文感知的药物事件
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.11
Noushin Salek Faramarzi, Meet Patel, Sai Harika Bandarupally, Ritwik Banerjee
{"title":"Context-aware Medication Event Extraction from Unstructured Text","authors":"Noushin Salek Faramarzi, Meet Patel, Sai Harika Bandarupally, Ritwik Banerjee","doi":"10.18653/v1/2023.clinicalnlp-1.11","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.11","url":null,"abstract":"Accurately capturing medication history is crucial in delivering high-quality medical care. The extraction of medication events from unstructured clinical notes, however, is challenging because the information is presented in complex narratives. We address this challenge by leveraging the newly released Contextualized Medication Event Dataset (CMED) as part of our participation in the 2022 National NLP Clinical Challenges (n2c2) shared task. Our study evaluates the performance of various pretrained language models in this task. Further, we find that data augmentation coupled with domain-specific training provides notable improvements. With experiments, we also underscore the importance of careful data preprocessing in medical event detection.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"188 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":"114930573","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
Textual Entailment for Temporal Dependency Graph Parsing 时间依赖图解析的文本蕴涵
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.25
Jiarui Yao, S. Bethard, Kristin Wright-Bettner, Eli Goldner, D. Harris, G. Savova
{"title":"Textual Entailment for Temporal Dependency Graph Parsing","authors":"Jiarui Yao, S. Bethard, Kristin Wright-Bettner, Eli Goldner, D. Harris, G. Savova","doi":"10.18653/v1/2023.clinicalnlp-1.25","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.25","url":null,"abstract":"We explore temporal dependency graph (TDG) parsing in the clinical domain. We leverage existing annotations on the THYME dataset to semi-automatically construct a TDG corpus. Then we propose a new natural language inference (NLI) approach to TDG parsing, and evaluate it both on general domain TDGs from wikinews and the newly constructed clinical TDG corpus. We achieve competitive performance on general domain TDGs with a much simpler model than prior work. On the clinical TDGs, our method establishes the first result of TDG parsing on clinical data with 0.79/0.88 micro/macro F1.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"30 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":"131706889","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
Improving Automatic KCD Coding: Introducing the KoDAK and an Optimized Tokenization Method for Korean Clinical Documents 改进KCD自动编码:介绍柯达和一种优化的韩文临床文件标记方法
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.12
Geunyeong Jeong, Juoh Sun, Seokwon Jeong, Hyunjin Shin, Harksoo Kim
{"title":"Improving Automatic KCD Coding: Introducing the KoDAK and an Optimized Tokenization Method for Korean Clinical Documents","authors":"Geunyeong Jeong, Juoh Sun, Seokwon Jeong, Hyunjin Shin, Harksoo Kim","doi":"10.18653/v1/2023.clinicalnlp-1.12","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.12","url":null,"abstract":"International Classification of Diseases (ICD) coding is the task of assigning a patient’s electronic health records into standardized codes, which is crucial for enhancing medical services and reducing healthcare costs. In Korea, automatic Korean Standard Classification of Diseases (KCD) coding has been hindered by limited resources, differences in ICD systems, and language-specific characteristics. Therefore, we construct the Korean Dataset for Automatic KCD coding (KoDAK) by collecting and preprocessing Korean clinical documents. In addition, we propose a tokenization method optimized for Korean clinical documents. Our experiments show that our proposed method outperforms Korean Medical BERT (KM-BERT) in Macro-F1 performance by 0.14%p while using fewer model parameters, demonstrating its effectiveness in Korean clinical documents.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"1 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":"131064709","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
Transfer Learning for Low-Resource Clinical Named Entity Recognition 低资源临床命名实体识别的迁移学习
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.53
Nevasini Sasikumar, Krishna Sri Ipsit Mantri
{"title":"Transfer Learning for Low-Resource Clinical Named Entity Recognition","authors":"Nevasini Sasikumar, Krishna Sri Ipsit Mantri","doi":"10.18653/v1/2023.clinicalnlp-1.53","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.53","url":null,"abstract":"We propose a transfer learning method that adapts a high-resource English clinical NER model to low-resource languages and domains using only small amounts of in-domain annotated data. Our approach involves translating in-domain datasets to English, fine-tuning the English model on the translated data, and then transferring it to the target language/domain. Experiments on Spanish, French, and conversational clinical text datasets show accuracy gains over models trained on target data alone. Our method achieves state-of-the-art performance and can enable clinical NLP in more languages and modalities with limited resources.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"1 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":"130605482","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