Clinical Natural Language Processing Workshop最新文献

UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition UMLS-KGI-BERT:用于生物医学实体识别的转换器中以数据为中心的知识集成

Clinical Natural Language Processing Workshop Pub Date : 2023-07-20 DOI: 10.48550/arXiv.2307.11170

Aidan Mannion, D. Schwab, L. Goeuriot

{"title":"UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition","authors":"Aidan Mannion, D. Schwab, L. Goeuriot","doi":"10.48550/arXiv.2307.11170","DOIUrl":"https://doi.org/10.48550/arXiv.2307.11170","url":null,"abstract":"Pre-trained transformer language models (LMs) have in recent years become the dominant paradigm in applied NLP. These models have achieved state-of-the-art performance on tasks such as information extraction, question answering, sentiment analysis, document classification and many others. In the biomedical domain, significant progress has been made in adapting this paradigm to NLP tasks that require the integration of domain-specific knowledge as well as statistical modelling of language. In particular, research in this area has focused on the question of how best to construct LMs that take into account not only the patterns of token distribution in medical text, but also the wealth of structured information contained in terminology resources such as the UMLS. This work contributes a data-centric paradigm for enriching the language representations of biomedical transformer-encoder LMs by extracting text sequences from the UMLS.This allows for graph-based learning objectives to be combined with masked-language pre-training. Preliminary results from experiments in the extension of pre-trained LMs as well as training from scratch show that this framework improves downstream performance on multiple biomedical and clinical Named Entity Recognition (NER) tasks. All pre-trained models, data processing pipelines and evaluation scripts will be made publicly available.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130819155","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

SummQA at MEDIQA-Chat 2023: In-Context Learning with GPT-4 for Medical Summarization 在MEDIQA-Chat 2023:语境学习与GPT-4医学总结

Clinical Natural Language Processing Workshop Pub Date : 2023-06-30 DOI: 10.48550/arXiv.2306.17384

Yash Mathur, Sanketh Rangreji, Raghav Kapoor, Medha Palavalli, Amanda Bertsch, Matthew R. Gormley

{"title":"SummQA at MEDIQA-Chat 2023: In-Context Learning with GPT-4 for Medical Summarization","authors":"Yash Mathur, Sanketh Rangreji, Raghav Kapoor, Medha Palavalli, Amanda Bertsch, Matthew R. Gormley","doi":"10.48550/arXiv.2306.17384","DOIUrl":"https://doi.org/10.48550/arXiv.2306.17384","url":null,"abstract":"Medical dialogue summarization is challenging due to the unstructured nature of medical conversations, the use of medical terminologyin gold summaries, and the need to identify key information across multiple symptom sets. We present a novel system for the Dialogue2Note Medical Summarization tasks in the MEDIQA 2023 Shared Task. Our approach for sectionwise summarization (Task A) is a two-stage process of selecting semantically similar dialogues and using the top-k similar dialogues as in-context examples for GPT-4. For full-note summarization (Task B), we use a similar solution with k=1. We achieved 3rd place in Task A (2nd among all teams), 4th place in Task B Division Wise Summarization (2nd among all teams), 15th place in Task A Section Header Classification (9th among all teams), and 8th place among all teams in Task B. Our results highlight the effectiveness of few-shot prompting for this task, though we also identify several weaknesses of prompting-based approaches. We compare GPT-4 performance with several finetuned baselines. We find that GPT-4 summaries are more abstractive and shorter. We make our code publicly available.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125094337","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: 4

UMASS_BioNLP at MEDIQA-Chat 2023: Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations? UMASS_BioNLP出席MEDIQA-Chat 2023: llm能否生成高质量的合成笔记型医患对话?

Clinical Natural Language Processing Workshop Pub Date : 2023-06-29 DOI: 10.48550/arXiv.2306.16931

Junda Wang, Zonghai Yao, Avijit Mitra, Samuel Osebe, Zhichao Yang, Hongfeng Yu

引用次数: 6

DS4DH at MEDIQA-Chat 2023: Leveraging SVM and GPT-3 Prompt Engineering for Medical Dialogue Classification and Summarization DS4DH在MEDIQA-Chat 2023:利用SVM和GPT-3提示工程进行医学对话分类和总结

Clinical Natural Language Processing Workshop Pub Date : 2023-06-12 DOI: 10.1101/2023.06.08.23291121

Boya Zhang, R. Mishra, D. Teodoro

引用次数: 1

Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning 基于快速提取健康社会决定因素的少次学习方法

Clinical Natural Language Processing Workshop Pub Date : 2023-06-12 DOI: 10.48550/arXiv.2306.07170

Giridhar Kaushik Ramachandran, Yujuan Fu, Bin Han, K. Lybarger, Nicholas J. Dobbins, Ozlem Uzuner, M. Yetisgen

引用次数: 1

IUTEAM1 at MEDIQA-Chat 2023: Is simple fine tuning effective for multi layer summarization of clinical conversations? IUTEAM1在MEDIQA-Chat 2023:简单的微调对临床对话的多层总结有效吗?

Clinical Natural Language Processing Workshop Pub Date : 2023-06-07 DOI: 10.48550/arXiv.2306.04328

Dhananjay Srivastava

引用次数: 1

Multilingual Clinical NER: Translation or Cross-lingual Transfer? 多语临床NER:翻译还是跨语迁移?

Clinical Natural Language Processing Workshop Pub Date : 2023-06-07 DOI: 10.48550/arXiv.2306.04384

X. Fontaine, Félix Gaschi, Parisa Rastin, Y. Toussaint

{"title":"Multilingual Clinical NER: Translation or Cross-lingual Transfer?","authors":"X. Fontaine, Félix Gaschi, Parisa Rastin, Y. Toussaint","doi":"10.48550/arXiv.2306.04384","DOIUrl":"https://doi.org/10.48550/arXiv.2306.04384","url":null,"abstract":"Natural language tasks like Named Entity Recognition (NER) in the clinical domain on non-English texts can be very time-consuming and expensive due to the lack of annotated data. Cross-lingual transfer (CLT) is a way to circumvent this issue thanks to the ability of multilingual large language models to be fine-tuned on a specific task in one language and to provide high accuracy for the same task in another language. However, other methods leveraging translation models can be used to perform NER without annotated data in the target language, by either translating the training set or test set. This paper compares cross-lingual transfer with these two alternative methods, to perform clinical NER in French and in German without any training data in those languages. To this end, we release MedNERF a medical NER test set extracted from French drug prescriptions and annotated with the same guidelines as an English dataset. Through extensive experiments on this dataset and on a German medical dataset (Frei and Kramer, 2021), we show that translation-based methods can achieve similar performance to CLT but require more care in their design. And while they can take advantage of monolingual clinical language models, those do not guarantee better results than large general-purpose multilingual models, whether with cross-lingual transfer or translation.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132483899","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

Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis 基于逻辑神经网络的话语分类:用于精神障碍诊断的可解释人工智能

Clinical Natural Language Processing Workshop Pub Date : 2023-06-06 DOI: 10.48550/arXiv.2306.03902

Yeldar Toleubay, Don Joven Agravante, Daiki Kimura, Baihan Lin, Djallel Bouneffouf, Michiaki Tatsubori

引用次数: 0

Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models 生成医学上准确的患者-提供者对话摘要:使用大型语言模型的多阶段方法

Clinical Natural Language Processing Workshop Pub Date : 2023-05-10 DOI: 10.48550/arXiv.2305.05982

Varun Nair, Elliot Schumacher, Anitha Kannan

{"title":"Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models","authors":"Varun Nair, Elliot Schumacher, Anitha Kannan","doi":"10.48550/arXiv.2305.05982","DOIUrl":"https://doi.org/10.48550/arXiv.2305.05982","url":null,"abstract":"A medical provider’s summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference for the patient. An effective summary is required to be coherent and accurately capture all the medically relevant information in the dialogue, despite the complexity of patient-generated language. Even minor inaccuracies in visit summaries (for example, summarizing “patient does not have a fever” when a fever is present) can be detrimental to the outcome of care for the patient.This paper tackles the problem of medical conversation summarization by discretizing the task into several smaller dialogue-understanding tasks that are sequentially built upon. First, we identify medical entities and their affirmations within the conversation to serve as building blocks. We study dynamically constructing few-shot prompts for tasks by conditioning on relevant patient information and use GPT-3 as the backbone for our experiments. We also develop GPT-derived summarization metrics to measure performance against reference summaries quantitatively. Both our human evaluation study and metrics for medical correctness show that summaries generated using this approach are clinically accurate and outperform the baseline approach of summarizing the dialog in a zero-shot, single-prompt setting.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134024764","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

GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning GersteinLab在MEDIQA-Chat 2023:通过微调和上下文学习从医患对话中总结临床笔记

Clinical Natural Language Processing Workshop Pub Date : 2023-05-08 DOI: 10.48550/arXiv.2305.05001

Xiangru Tang, Andrew Tran, Jeffrey Tan, Mark B. Gerstein

引用次数: 4