Clinical Natural Language Processing Workshop最新文献_第3页

Cancer Registry Information Extraction via Transfer Learning 基于迁移学习的癌症登记信息提取

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.22

Yan-Jie Lin, Hong-Jie Dai, You-Chen Zhang, Chung-Yang Wu, Yu-Cheng Chang, Pin-Jou Lu, Chih-Jen Huang, Yu-Tsang Wang, H. Hsieh, K. Chao, T. Liu, I. Chang, Yi-Hsin Connie Yang, Ti-Hao Wang, Ko-Jiunn Liu, Li‐Tzong Chen, Sheau-Fang Yang

{"title":"Cancer Registry Information Extraction via Transfer Learning","authors":"Yan-Jie Lin, Hong-Jie Dai, You-Chen Zhang, Chung-Yang Wu, Yu-Cheng Chang, Pin-Jou Lu, Chih-Jen Huang, Yu-Tsang Wang, H. Hsieh, K. Chao, T. Liu, I. Chang, Yi-Hsin Connie Yang, Ti-Hao Wang, Ko-Jiunn Liu, Li‐Tzong Chen, Sheau-Fang Yang","doi":"10.18653/v1/2020.clinicalnlp-1.22","DOIUrl":"https://doi.org/10.18653/v1/2020.clinicalnlp-1.22","url":null,"abstract":"A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and F-measure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134140508","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: 2

Learning from Unlabelled Data for Clinical Semantic Textual Similarity 从未标记数据中学习临床语义文本相似度

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.25

Yuxia Wang, K. Verspoor, Timothy Baldwin

引用次数: 21

BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition 用于临床命名实体识别的葡萄牙语神经语言模型

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.7

Elisa Terumi Rubel Schneider, João Vitor Andrioli de Souza, J. Knafou, Lucas E. S. Oliveira, J. Copara, Yohan Bonescki Gumiel, L. A. F. D. Oliveira, E. Paraiso, D. Teodoro, C. M. Barra

{"title":"BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition","authors":"Elisa Terumi Rubel Schneider, João Vitor Andrioli de Souza, J. Knafou, Lucas E. S. Oliveira, J. Copara, Yohan Bonescki Gumiel, L. A. F. D. Oliveira, E. Paraiso, D. Teodoro, C. M. Barra","doi":"10.18653/v1/2020.clinicalnlp-1.7","DOIUrl":"https://doi.org/10.18653/v1/2020.clinicalnlp-1.7","url":null,"abstract":"With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72%, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115090740","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: 50

Relative and Incomplete Time Expression Anchoring for Clinical Text 临床文本的相对与不完全时间表达锚定

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.14

Louise Dupuis, N. Bergou, Hegler C. Tissot, S. Velupillai

引用次数: 1

Classification of Syncope Cases in Norwegian Medical Records 挪威医疗记录中晕厥病例的分类

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.9

I. Pilán, P. Brekke, F. A. Dahl, T. Gundersen, Haldor Husby, Ø. Nytrø, Lilja Øvrelid

引用次数: 1

Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics 临床分析中命名实体识别和关联提取任务的预训练变压器联合学习

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.26

Miao Chen, Ganhui Lan, Fang Du, V. Lobanov

{"title":"Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics","authors":"Miao Chen, Ganhui Lan, Fang Du, V. Lobanov","doi":"10.18653/v1/2020.clinicalnlp-1.26","DOIUrl":"https://doi.org/10.18653/v1/2020.clinicalnlp-1.26","url":null,"abstract":"In drug development, protocols define how clinical trials are conducted, and are therefore of paramount importance. They contain key patient-, investigator-, medication-, and study-related information, often elaborated in different sections in the protocol texts. Granular-level parsing on large quantity of existing protocols can accelerate clinical trial design and provide actionable insights into trial optimization. Here, we report our progresses in using deep learning NLP algorithms to enable automated protocol analytics. In particular, we combined a pre-trained BERT transformer model with joint-learning strategies to simultaneously identify clinically relevant entities (i.e. Named Entity Recognition) and extract the syntactic relations between these entities (i.e. Relation Extraction) from the eligibility criteria section in protocol texts. When comparing to standalone NER and RE models, our joint-learning strategy can effectively improve the performance of RE task while retaining similarly high NER performance, likely due to the synergy of optimizing toward both tasks’ objectives via shared parameters. The derived NLP model provides an end-to-end solution to convert unstructured protocol texts into structured data source, which will be embedded into a comprehensive clinical analytics workflow for downstream trial design missions such like patient population extraction, patient enrollment rate estimation, and protocol amendment prediction.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130215124","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: 12

Various Approaches for Predicting Stroke Prognosis using Magnetic Resonance Imaging Text Records 使用磁共振成像文本记录预测脑卒中预后的各种方法

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.1

Tak-Sung Heo, Chulho Kim, J. Choi, Y. Jeong, Yu-Seop Kim

引用次数: 2

Extracting Relations between Radiotherapy Treatment Details 放射治疗细节之间的关系提取

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.21

D. Bitterman, T. Miller, D. Harris, Chen Lin, S. Finan, J. Warner, R. Mak, G. Savova

引用次数: 5

Comparison of Machine Learning Methods for Multi-label Classification of Nursing Education and Licensure Exam Questions 护理教育与执照考试多标签分类的机器学习方法比较

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.10

J. Langton, K. Srihasam, Junlin Jiang

引用次数: 1

Assessment of DistilBERT performance on Named Entity Recognition task for the detection of Protected Health Information and medical concepts 用于检测受保护健康信息和医学概念的命名实体识别任务中的蒸馏器性能评估

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.18

Macarious Abadeer

引用次数: 13