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Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations最新文献

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Joint Summarization-Entailment Optimization for Consumer Health Question Understanding 联合总结-消费者健康问题理解的蕴涵优化
Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.NLPMC-1.8
Khalil Mrini, Franck Dernoncourt, Walter Chang, E. Farcas, Ndapandula Nakashole
{"title":"Joint Summarization-Entailment Optimization for Consumer Health Question Understanding","authors":"Khalil Mrini, Franck Dernoncourt, Walter Chang, E. Farcas, Ndapandula Nakashole","doi":"10.18653/V1/2021.NLPMC-1.8","DOIUrl":"https://doi.org/10.18653/V1/2021.NLPMC-1.8","url":null,"abstract":"Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems. We propose a novel data-augmented and simple joint learning approach combining question summarization and Recognizing Question Entailment (RQE) in the medical domain. Our data augmentation approach enables to use just one dataset for joint learning. We show improvements on both tasks across four biomedical datasets in accuracy (+8%), ROUGE-1 (+2.5%) and human evaluation scores. Human evaluation shows joint learning generates faithful and informative summaries. Finally, we release our code, the two question summarization datasets extracted from a large-scale medical dialogue dataset, as well as our augmented datasets.","PeriodicalId":191333,"journal":{"name":"Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129236499","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: 9
Assertion Detection in Clinical Notes: Medical Language Models to the Rescue? 临床笔记中的断言检测:医学语言模型的拯救?
Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.NLPMC-1.5
Betty van Aken, Ivana Trajanovska, A. Siu, M. Mayrdorfer, K. Budde, A. Loeser
{"title":"Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?","authors":"Betty van Aken, Ivana Trajanovska, A. Siu, M. Mayrdorfer, K. Budde, A. Loeser","doi":"10.18653/V1/2021.NLPMC-1.5","DOIUrl":"https://doi.org/10.18653/V1/2021.NLPMC-1.5","url":null,"abstract":"In order to provide high-quality care, health professionals must efficiently identify the presence, possibility, or absence of symptoms, treatments and other relevant entities in free-text clinical notes. Such is the task of assertion detection - to identify the assertion class (present, possible, absent) of an entity based on textual cues in unstructured text. We evaluate state-of-the-art medical language models on the task and show that they outperform the baselines in all three classes. As transferability is especially important in the medical domain we further study how the best performing model behaves on unseen data from two other medical datasets. For this purpose we introduce a newly annotated set of 5,000 assertions for the publicly available MIMIC-III dataset. We conclude with an error analysis that reveals situations in which the models still go wrong and points towards future research directions.","PeriodicalId":191333,"journal":{"name":"Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129835503","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: 11
Gathering Information and Engaging the User ComBot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions 收集信息和参与用户组合机器人:一个基于任务的，偶然的对话模型的医患互动
Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.NLPMC-1.3
Anna A. Liednikova, Ph. Jolivet, Alexandre Durand-Salmon, Claire Gardent
{"title":"Gathering Information and Engaging the User ComBot: A Task-Based, Serendipitous Dialog Model for Patient-Doctor Interactions","authors":"Anna A. Liednikova, Ph. Jolivet, Alexandre Durand-Salmon, Claire Gardent","doi":"10.18653/V1/2021.NLPMC-1.3","DOIUrl":"https://doi.org/10.18653/V1/2021.NLPMC-1.3","url":null,"abstract":"We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement and address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot.","PeriodicalId":191333,"journal":{"name":"Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131859218","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: 5
Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization 迈向自动化医疗记录:诊所访问对话2笔记句子对齐和片段摘要
Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.NLPMC-1.2
Wen-wai Yim, M. Yetisgen
{"title":"Towards Automating Medical Scribing : Clinic Visit Dialogue2Note Sentence Alignment and Snippet Summarization","authors":"Wen-wai Yim, M. Yetisgen","doi":"10.18653/V1/2021.NLPMC-1.2","DOIUrl":"https://doi.org/10.18653/V1/2021.NLPMC-1.2","url":null,"abstract":"Medical conversations from patient visits are routinely summarized into clinical notes for documentation of clinical care. The automatic creation of clinical note is particularly challenging given that it requires summarization over spoken language and multiple speaker turns; as well, clinical notes include highly technical semi-structured text. In this paper, we describe our corpus creation method and baseline systems for two NLP tasks, clinical dialogue2note sentence alignment and clinical dialogue2note snippet summarization. These two systems, as well as other models created from such a corpus, may be incorporated as parts of an overall end-to-end clinical note generation system.","PeriodicalId":191333,"journal":{"name":"Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122014527","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: 22
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