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Interactive Span Recommendation for Biomedical Text 生物医学文本交互式跨度推荐
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.40
Louis Blankemeier, Theodore Zhao, Robert Tinn, Sid Kiblawi, Yu Gu, Akshay Chaudhari, Hoifung Poon, Sheng Zhang, Mu-Hsin Wei, J. Preston
{"title":"Interactive Span Recommendation for Biomedical Text","authors":"Louis Blankemeier, Theodore Zhao, Robert Tinn, Sid Kiblawi, Yu Gu, Akshay Chaudhari, Hoifung Poon, Sheng Zhang, Mu-Hsin Wei, J. Preston","doi":"10.18653/v1/2023.clinicalnlp-1.40","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.40","url":null,"abstract":"Motivated by the scarcity of high-quality labeled biomedical text, as well as the success of data programming, we introduce KRISS-Search. By leveraging the Unified Medical Language Systems (UMLS) ontology, KRISS-Search addresses an interactive few-shot span recommendation task that we propose. We first introduce unsupervised KRISS-Search and show that our method outperforms existing methods in identifying spans that are semantically similar to a given span of interest, with >50% AUPRC improvement relative to PubMedBERT. We then introduce supervised KRISS-Search, which leverages human interaction to improve the notion of similarity used by unsupervised KRISS-Search. Through simulated human feedback, we demonstrate an enhanced F1 score of 0.68 in classifying spans as semantically similar or different in the low-label setting, outperforming PubMedBERT by 2 F1 points. Finally, supervised KRISS-Search demonstrates competitive or superior performance compared to PubMedBERT in few-shot biomedical named entity recognition (NER) across five benchmark datasets, with an average improvement of 5.6 F1 points. We envision KRISS-Search increasing the efficiency of programmatic data labeling and also providing broader utility as an interactive biomedical search engine.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"72 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":"130157386","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
Hindi Chatbot for Supporting Maternal and Child Health Related Queries in Rural India 用于支持印度农村妇幼保健相关查询的印地语聊天机器人
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.9
Ritwik Mishra, Simranjeet Singh, Jasmeet Kaur, Pushpendra Singh, R. Shah
{"title":"Hindi Chatbot for Supporting Maternal and Child Health Related Queries in Rural India","authors":"Ritwik Mishra, Simranjeet Singh, Jasmeet Kaur, Pushpendra Singh, R. Shah","doi":"10.18653/v1/2023.clinicalnlp-1.9","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.9","url":null,"abstract":"In developing countries like India, doctors and healthcare professionals working in public health spend significant time answering health queries that are fact-based and repetitive. Therefore, we propose an automated way to answer maternal and child health-related queries. A database of Frequently Asked Questions (FAQs) and their corresponding answers generated by experts is curated from rural health workers and young mothers. We develop a Hindi chatbot that identifies k relevant Question and Answer (QnA) pairs from the database in response to a healthcare query (q) written in Devnagri script or Hindi-English (Hinglish) code-mixed script. The curated database covers 80% of all the queries that a user of our study is likely to ask. We experimented with (i) rule-based methods, (ii) sentence embeddings, and (iii) a paraphrasing classifier, to calculate the q-Q similarity. We observed that paraphrasing classifier gives the best result when trained first on an open-domain text and then on the healthcare domain. Our chatbot uses an ensemble of all three approaches. We observed that if a given q can be answered using the database, then our chatbot can provide at least one relevant QnA pair among its top three suggestions for up to 70% of the queries.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"29 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":"133270432","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
Large Scale Sequence-to-Sequence Models for Clinical Note Generation from Patient-Doctor Conversations 从医患对话中生成临床记录的大规模序列到序列模型
Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.18
Gagandeep Singh, Yue Pan, Jesús Andrés-Ferrer, Miguel A. del Agua, Frank Diehl, Joel Pinto, Paul Vozila
{"title":"Large Scale Sequence-to-Sequence Models for Clinical Note Generation from Patient-Doctor Conversations","authors":"Gagandeep Singh, Yue Pan, Jesús Andrés-Ferrer, Miguel A. del Agua, Frank Diehl, Joel Pinto, Paul Vozila","doi":"10.18653/v1/2023.clinicalnlp-1.18","DOIUrl":"https://doi.org/10.18653/v1/2023.clinicalnlp-1.18","url":null,"abstract":"We present our work on building large scale sequence-to-sequence models for generating clinical note from patient-doctor conversation. This is formulated as an abstractive summarization task for which we use encoder-decoder transformer model with pointer-generator. We discuss various modeling enhancements to this baseline model which include using subword and multiword tokenization scheme, prefixing the targets with a chain-of-clinical-facts, and training with contrastive loss that is defined over various candidate summaries. We also use flash attention during training and query chunked attention during inference to be able to process long input and output sequences and to improve computational efficiency. Experiments are conducted on a dataset containing about 900K encounters from around 1800 healthcare providers covering 27 specialties. The results are broken down into primary care and non-primary care specialties. Consistent accuracy improvements are observed across both of these categories.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"58 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":"132122932","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
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
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