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Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task最新文献

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Transfer Learning for Health-related Twitter Data 健康相关Twitter数据的迁移学习
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3212
A. Dirkson, S. Verberne
{"title":"Transfer Learning for Health-related Twitter Data","authors":"A. Dirkson, S. Verberne","doi":"10.18653/v1/W19-3212","DOIUrl":"https://doi.org/10.18653/v1/W19-3212","url":null,"abstract":"Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data. This paper describes the methods developed by team TMRLeiden for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task. Our methods use state-of-the-art transfer learning methods to classify, extract and normalise adverse drug effects (ADRs) and to classify personal health mentions from health-related tweets. The code and fine-tuned models are publicly available.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"365 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":"123504449","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: 14
Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research 从讣告中提取亲属关系以增强基因研究电子健康记录
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3201
Kai He, Jialun Wu, Xiaoyong Ma, Chong Zhang, Ming Huang, Chen Li, Lixia Yao
{"title":"Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research","authors":"Kai He, Jialun Wu, Xiaoyong Ma, Chong Zhang, Ming Huang, Chen Li, Lixia Yao","doi":"10.18653/v1/W19-3201","DOIUrl":"https://doi.org/10.18653/v1/W19-3201","url":null,"abstract":"Claims database and electronic health records database do not usually capture kinship or family relationship information, which is imperative for genetic research. We identify online obituaries as a new data source and propose a special named entity recognition and relation extraction solution to extract names and kinships from online obituaries. Built on 1,809 annotated obituaries and a novel tagging scheme, our joint neural model achieved macro-averaged precision, recall and F measure of 72.69%, 78.54% and 74.93%, and micro-averaged precision, recall and F measure of 95.74%, 98.25% and 96.98% using 57 kinships with 10 or more examples in a 10-fold cross-validation experiment. The model performance improved dramatically when trained with 34 kinships with 50 or more examples. Leveraging additional information such as age, death date, birth date and residence mentioned by obituaries, we foresee a promising future of supplementing EHR databases with comprehensive and accurate kinship information for genetic research.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"3 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":"115738485","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
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention 带有多头自关注的药品不良反应提及推文的检测与提取
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3214
Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang
{"title":"Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention","authors":"Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang","doi":"10.18653/v1/W19-3214","DOIUrl":"https://doi.org/10.18653/v1/W19-3214","url":null,"abstract":"This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop. We enhance tweet representation with a language model and distinguish the importance of different words with Multi-Head Self-Attention. In addition, transfer learning is exploited to make up for the data shortage. Our system achieved competitive results on both tasks with an F1-score of 0.5718 for task 1 and 0.653 (overlap) / 0.357 (strict) for task 2.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"33 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":"123698663","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
Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media 使用机器学习和深度学习方法在社交媒体中查找药物不良反应的提及
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3216
Pilar López Úbeda, Manuel Carlos Díaz Galiano, Maite Martin, L. Ureña López
{"title":"Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media","authors":"Pilar López Úbeda, Manuel Carlos Díaz Galiano, Maite Martin, L. Ureña López","doi":"10.18653/v1/W19-3216","DOIUrl":"https://doi.org/10.18653/v1/W19-3216","url":null,"abstract":"Over time the use of social networks is becoming very popular platforms for sharing health related information. Social Media Mining for Health Applications (SMM4H) provides tasks such as those described in this document to help manage information in the health domain. This document shows the first participation of the SINAI group. We study approaches based on machine learning and deep learning to extract adverse drug reaction mentions from Twitter. The results obtained in the tasks are encouraging, we are close to the average of all participants and even above in some cases.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"3 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":"121038577","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: 6
Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation 识别不良药物事件提到的推文使用细心，错位，和汇总的医疗表示
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3209
Xinyan Zhao, D. Yu, V.G.Vinod Vydiswaran
{"title":"Identifying Adverse Drug Events Mentions in Tweets Using Attentive, Collocated, and Aggregated Medical Representation","authors":"Xinyan Zhao, D. Yu, V.G.Vinod Vydiswaran","doi":"10.18653/v1/W19-3209","DOIUrl":"https://doi.org/10.18653/v1/W19-3209","url":null,"abstract":"Identifying mentions of medical concepts in social media is challenging because of high variability in free text. In this paper, we propose a novel neural network architecture, the Collocated LSTM with Attentive Pooling and Aggregated representation (CLAPA), that integrates a bidirectional LSTM model with attention and pooling strategy and utilizes the collocation information from training data to improve the representation of medical concepts. The collocation and aggregation layers improve the model performance on the task of identifying mentions of adverse drug events (ADE) in tweets. Using the dataset made available as part of the workshop shared task, we show that careful selection of neighborhood contexts can help uncover useful local information and improve the overall medical concept representation.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"53 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":"123158940","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
Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety 应用PHQ-4被动诊断抑郁和焦虑
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3205
Fionn Delahunty, R. Johansson, Mihael Arcan
{"title":"Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety","authors":"Fionn Delahunty, R. Johansson, Mihael Arcan","doi":"10.18653/v1/W19-3205","DOIUrl":"https://doi.org/10.18653/v1/W19-3205","url":null,"abstract":"Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. In this work, we develop and evaluate a multilabel, multidimensional deep neural network designed to predict PHQ-4 scores based on individuals written text. Our system outperforms random baseline metrics and provides a novel approach to how we can predict psychometric scores from written text. Additionally, we explore how this architecture can be applied to analyse social media data.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"35 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":"124434440","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: 6
MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter MIDAS@SMM4H-2019:从Twitter上识别药物不良反应和个人健康经历
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3223
Debanjan Mahata, Sarthak Anand, Haimin Zhang, Simra Shahid, Laiba Mehnaz, Yaman Kumar Singla, R. Shah
{"title":"MIDAS@SMM4H-2019: Identifying Adverse Drug Reactions and Personal Health Experience Mentions from Twitter","authors":"Debanjan Mahata, Sarthak Anand, Haimin Zhang, Simra Shahid, Laiba Mehnaz, Yaman Kumar Singla, R. Shah","doi":"10.18653/v1/W19-3223","DOIUrl":"https://doi.org/10.18653/v1/W19-3223","url":null,"abstract":"In this paper, we present our approach and the system description for the Social Media Mining for Health Applications (SMM4H) Shared Task 1,2 and 4 (2019). Our main contribution is to show the effectiveness of Transfer Learning approaches like BERT and ULMFiT, and how they generalize for the classification tasks like identification of adverse drug reaction mentions and reporting of personal health problems in tweets. We show the use of stacked embeddings combined with BLSTM+CRF tagger for identifying spans mentioning adverse drug reactions in tweets. We also show that these approaches perform well even with imbalanced dataset in comparison to undersampling and oversampling.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"3 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":"129202576","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: 10
Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo 基于ELMo的推文药物不良反应提及检测
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3221
S. Sarabadani
{"title":"Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo","authors":"S. Sarabadani","doi":"10.18653/v1/W19-3221","DOIUrl":"https://doi.org/10.18653/v1/W19-3221","url":null,"abstract":"This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics. For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR. Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"59 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":"116967582","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: 6
Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3222
Parsa Bagherzadeh, Nadia Sheikh, S. Bergler
{"title":"Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4","authors":"Parsa Bagherzadeh, Nadia Sheikh, S. Bergler","doi":"10.18653/v1/W19-3222","DOIUrl":"https://doi.org/10.18653/v1/W19-3222","url":null,"abstract":"CLaC labs participated in Task 1 and 4 of SMM4H 2019. We pursed two main objectives in our submission. First we tried to use some textual features in a deep net framework, and second, the potential use of more than one word embedding was tested. The results seem positively affected by the proposed architectures.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"130 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":"124103006","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
Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019 ACL 2019第四届健康社交媒体挖掘(SMM4H)共享任务概述
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 1900-01-01 DOI: 10.18653/v1/W19-3203
D. Weissenbacher, A. Sarker, A. Magge, A. Daughton, K. O’Connor, Michael J. Paul, G. Gonzalez-Hernandez
{"title":"Overview of the Fourth Social Media Mining for Health (SMM4H) Shared Tasks at ACL 2019","authors":"D. Weissenbacher, A. Sarker, A. Magge, A. Daughton, K. O’Connor, Michael J. Paul, G. Gonzalez-Hernandez","doi":"10.18653/v1/W19-3203","DOIUrl":"https://doi.org/10.18653/v1/W19-3203","url":null,"abstract":"The number of users of social media continues to grow, with nearly half of adults worldwide and two-thirds of all American adults using social networking. Advances in automated data processing, machine learning and NLP present the possibility of utilizing this massive data source for biomedical and public health applications, if researchers address the methodological challenges unique to this media. We present the Social Media Mining for Health Shared Tasks collocated with the ACL at Florence in 2019, which address these challenges for health monitoring and surveillance, utilizing state of the art techniques for processing noisy, real-world, and substantially creative language expressions from social media users. For the fourth execution of this challenge, we proposed four different tasks. Task 1 asked participants to distinguish tweets reporting an adverse drug reaction (ADR) from those that do not. Task 2, a follow-up to Task 1, asked participants to identify the span of text in tweets reporting ADRs. Task 3 is an end-to-end task where the goal was to first detect tweets mentioning an ADR and then map the extracted colloquial mentions of ADRs in the tweets to their corresponding standard concept IDs in the MedDRA vocabulary. Finally, Task 4 asked participants to classify whether a tweet contains a personal mention of one’s health, a more general discussion of the health issue, or is an unrelated mention. A total of 34 teams from around the world registered and 19 teams from 12 countries submitted a system run. We summarize here the corpora for this challenge which are freely available at https://competitions.codalab.org/competitions/22521, and present an overview of the methods and the results of the competing systems.","PeriodicalId":265570,"journal":{"name":"Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task","volume":"286 2 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":"123729701","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: 85
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