Clinical Natural Language Processing Workshop最新文献_第4页

Analyzing Text Specific vs Blackbox Fairness Algorithms in Multimodal Clinical NLP 多模态临床NLP中文本特定与黑盒公平性算法的分析

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.33

John Chen, Ian Berlot-Attwell, Safwan Hossain, Xindi Wang, Frank Rudzicz

引用次数: 9

The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish 智利等候名单语料库:西班牙语临床命名实体识别的新资源

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.32

P. Baez, F. Villena, Matías Rojas, Manuel Durán, J. Dunstan

{"title":"The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish","authors":"P. Baez, F. Villena, Matías Rojas, Manuel Durán, J. Dunstan","doi":"10.18653/v1/2020.clinicalnlp-1.32","DOIUrl":"https://doi.org/10.18653/v1/2020.clinicalnlp-1.32","url":null,"abstract":"In this work we describe the Waiting List Corpus consisting of de-identified referrals for several specialty consultations from the waiting list in Chilean public hospitals. A subset of 900 referrals was manually annotated with 9,029 entities, 385 attributes, and 284 pairs of relations with clinical relevance. A trained medical doctor annotated these referrals, and then together with other three researchers, consolidated each of the annotations. The annotated corpus has nested entities, with 32.2% of entities embedded in other entities. We use this annotated corpus to obtain preliminary results for Named Entity Recognition (NER). The best results were achieved by using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group. NER models applied to this corpus can leverage statistics of diseases and pending procedures within this waiting list. This work constitutes the first annotated corpus using clinical narratives from Chile, and one of the few for the Spanish language. The annotated corpus, the clinical word embeddings, and the annotation guidelines are freely released to the research community.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"2 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":"116389448","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: 21

Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries 利用多模态特征一致性检测临床摘要中的对抗性样本

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.29

Wenjie Wang, Youngja Park, Taesung Lee, Ian Molloy, Pengfei Tang, Li Xiong

{"title":"Utilizing Multimodal Feature Consistency to Detect Adversarial Examples on Clinical Summaries","authors":"Wenjie Wang, Youngja Park, Taesung Lee, Ian Molloy, Pengfei Tang, Li Xiong","doi":"10.18653/v1/2020.clinicalnlp-1.29","DOIUrl":"https://doi.org/10.18653/v1/2020.clinicalnlp-1.29","url":null,"abstract":"Recent studies have shown that adversarial examples can be generated by applying small perturbations to the inputs such that the well- trained deep learning models will misclassify. With the increasing number of safety and security-sensitive applications of deep learn- ing models, the robustness of deep learning models has become a crucial topic. The robustness of deep learning models for health- care applications is especially critical because the unique characteristics and the high financial interests of the medical domain make it more sensitive to adversarial attacks. Among the modalities of medical data, the clinical summaries have higher risks to be attacked because they are generated by third-party companies. As few works studied adversarial threats on clinical summaries, in this work we first apply adversarial attack to clinical summaries of electronic health records (EHR) to show the text-based deep learning systems are vulnerable to adversarial examples. Secondly, benefiting from the multi-modality of the EHR dataset, we propose a novel defense method, MATCH (Multimodal feATure Consistency cHeck), which leverages the consistency between multiple modalities in the data to defend against adversarial examples on a single modality. Our experiments demonstrate the effectiveness of MATCH on a hospital readmission prediction task comparing with baseline methods.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"10 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":"127462994","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

Advancing Seq2seq with Joint Paraphrase Learning 用联合释义学习推进Seq2seq

Clinical Natural Language Processing Workshop Pub Date : 2020-11-01 DOI: 10.18653/v1/2020.clinicalnlp-1.30

So Yeon Min, Preethi Raghavan, Peter Szolovits

引用次数: 5

PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation 通过数据增强提高临床文本去识别模型的泛化

Clinical Natural Language Processing Workshop Pub Date : 2020-10-11 DOI: 10.18653/v1/2020.clinicalnlp-1.23

Xiang Yue, Shuang Zhou

引用次数: 6

Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder 基于符号转换器编码器的瑞典处方信息提取

Clinical Natural Language Processing Workshop Pub Date : 2020-10-10 DOI: 10.18653/v1/2020.clinicalnlp-1.5

John N. Pougue Biyong, Bo Wang, Terry Lyons, A. Nevado-Holgado

引用次数: 2

Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer’s Disease (AD) using Mental Health Records: a Classification Approach 用精神健康记录区分路易体痴呆(DLB)和阿尔茨海默病(AD):一种分类方法

Clinical Natural Language Processing Workshop Pub Date : 2020-10-06 DOI: 10.18653/V1/2020.CLINICALNLP-1.19

Zixu Wang, Julia Ive, S. Moylett, C. Mueller, R. Cardinal, S. Velupillai, J. O'Brien, R. Stewart

{"title":"Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer’s Disease (AD) using Mental Health Records: a Classification Approach","authors":"Zixu Wang, Julia Ive, S. Moylett, C. Mueller, R. Cardinal, S. Velupillai, J. O'Brien, R. Stewart","doi":"10.18653/V1/2020.CLINICALNLP-1.19","DOIUrl":"https://doi.org/10.18653/V1/2020.CLINICALNLP-1.19","url":null,"abstract":"While Dementia with Lewy Bodies (DLB) is the second most common type of neurodegenerative dementia following Alzheimer’s Disease (AD), it is difficult to distinguish from AD. We propose a method for DLB detection by using mental health record (MHR) documents from a (3-month) period before a patient has been diagnosed with DLB or AD. Our objective is to develop a model that could be clinically useful to differentiate between DLB and AD across datasets from different healthcare institutions. We cast this as a classification task using Convolutional Neural Network (CNN), an efficient neural model for text classification. We experiment with different representation models, and explore the features that contribute to model performances. In addition, we apply temperature scaling, a simple but efficient model calibration method, to produce more reliable predictions. We believe the proposed method has important potential for clinical applications using routine healthcare records, and for generalising to other relevant clinical record datasets. To the best of our knowledge, this is the first attempt to distinguish DLB from AD using mental health records, and to improve the reliability of DLB predictions.","PeriodicalId":216954,"journal":{"name":"Clinical Natural Language Processing Workshop","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132521537","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

BERT-XML: Large Scale Automated ICD Coding Using BERT Pretraining 使用BERT预训练的大规模自动ICD编码

Clinical Natural Language Processing Workshop Pub Date : 2020-05-26 DOI: 10.18653/v1/2020.clinicalnlp-1.3

Zachariah Zhang, Jingshu Liu, N. Razavian

引用次数: 55

HealthMavericks@MEDIQA-Chat 2023: Benchmarking different Transformer based models for Clinical Dialogue Summarization HealthMavericks@MEDIQA-Chat 2023:基于临床对话总结的不同Transformer模型的基准测试

Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.50

Kunal Suri, Saumajit Saha, Ashutosh Kumar Singh

引用次数: 1

clulab at MEDIQA-Chat 2023: Summarization and classification of medical dialogues MEDIQA-Chat 2023:医学对话的总结和分类

Clinical Natural Language Processing Workshop Pub Date : 1900-01-01 DOI: 10.18653/v1/2023.clinicalnlp-1.19

Kadir Bulut Özler, S. Bethard

引用次数: 1