CEUR workshop proceedings最新文献_第3页

Personalized Health Knowledge Graph. 个性化健康知识图谱。

CEUR workshop proceedings Pub Date : 2018-10-01

Amelie Gyrard, Manas Gaur, Saeedeh Shekarpour, Krishnaprasad Thirunarayan, Amit Sheth

{"title":"Personalized Health Knowledge Graph.","authors":"Amelie Gyrard, Manas Gaur, Saeedeh Shekarpour, Krishnaprasad Thirunarayan, Amit Sheth","doi":"","DOIUrl":"","url":null,"abstract":"Our current health applications do not adequately take into account contextual and personalized knowledge about patients. In order to design \"Personalized Coach for Healthcare\" applications to manage chronic diseases, there is a need to create a Personalized Healthcare Knowledge Graph (PHKG) that takes into consideration a patient's health condition (personalized knowledge) and enriches that with contextualized knowledge from environmental sensors and Web of Data (e.g., symptoms and treatments for diseases). To develop PHKG, aggregating knowledge from various heterogeneous sources such as the Internet of Things (IoT) devices, clinical notes, and Electronic Medical Records (EMRs) is necessary. In this paper, we explain the challenges of collecting, managing, analyzing, and integrating patients' health data from various sources in order to synthesize and deduce meaningful information embodying the vision of the Data, Information, Knowledge, and Wisdom (DIKW) pyramid. Furthermore, we sketch a solution that combines: 1) IoT data analytics, and 2) explicit knowledge and illustrate it using three chronic disease use cases - asthma, obesity, and Parkinson's.","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"2317 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8532078/pdf/nihms-1743812.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39551742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards automated pain detection in children using facial and electrodermal activity 利用面部和皮肤电活动实现儿童疼痛自动检测

CEUR workshop proceedings Pub Date : 2018-07-13 DOI: 10.1007/978-3-030-12738-1_13

Xiaojing Xu, Busra T. Susam, H. Nezamfar, K. Craig, Damaris Diaz, Jeannie S. Huang, M. Goodwin, M. Akçakaya, V. D. Sa

引用次数: 12

Automated Pain Detection in Facial Videos of Children using Human-Assisted Transfer Learning. 使用人类辅助迁移学习的儿童面部视频中的自动疼痛检测。

CEUR workshop proceedings Pub Date : 2018-07-01

Xiaojing Xu, Kenneth D Craig, Damaris Diaz, Matthew S Goodwin, Murat Akcakaya, Büşra Tuğçe Susam, Jeannie S Huang, Virginia R de Sa

{"title":"Automated Pain Detection in Facial Videos of Children using Human-Assisted Transfer Learning.","authors":"Xiaojing Xu, Kenneth D Craig, Damaris Diaz, Matthew S Goodwin, Murat Akcakaya, Büşra Tuğçe Susam, Jeannie S Huang, Virginia R de Sa","doi":"","DOIUrl":"","url":null,"abstract":"Accurately determining pain levels in children is difficult, even for trained professionals and parents. Facial activity provides sensitive and specific information about pain, and computer vision algorithms have been developed to automatically detect Facial Action Units (AUs) defined by the Facial Action Coding System (FACS). Our prior work utilized information from computer vision, i.e., automatically detected facial AUs, to develop classifiers to distinguish between pain and no-pain conditions. However, application of pain/no-pain classifiers based on automated AU codings across different environmental domains results in diminished performance. In contrast, classifiers based on manually coded AUs demonstrate reduced environmentally-based variability in performance. In this paper, we train a machine learning model to recognize pain using AUs coded by a computer vision system embedded in a software package called iMotions. We also study the relationship between iMotions (automatically) and human (manually) coded AUs. We find that AUs coded automatically are different from those coded by a human trained in the FACS system, and that the human coder is less sensitive to environmental changes. To improve classification performance in the current work, we applied transfer learning by training another machine learning model to map automated AU codings to a subspace of manual AU codings to enable more robust pain recognition performance when only automatically coded AUs are available for the test data. With this transfer learning method, we improved the Area Under the ROC Curve (AUC) on independent data from new participants in our target domain from 0.67 to 0.72.","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"2142 ","pages":"10-21"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352979/pdf/nihms-1001649.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41164655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated pain detection in facial videos of children using human-assisted transfer learning 使用人工辅助迁移学习的儿童面部视频中的自动疼痛检测

CEUR workshop proceedings Pub Date : 2018-07-01 DOI: 10.1007/978-3-030-12738-1_12

Xiaojing Xu, K. Craig, Damaris Diaz, M. Goodwin, M. Akçakaya, Busra T. Susam, Jeannie S. Huang, V. D. Sa

引用次数: 24

Towards Automated Pain Detection in Children using Facial and Electrodermal Activity. 使用面部和皮肤电活动实现儿童疼痛的自动检测。

CEUR workshop proceedings Pub Date : 2018-07-01

Xiaojing Xu, Büsra Tuğce Susam, Hooman Nezamfar, Damaris Diaz, Kenneth D Craig, Matthew S Goodwin, Murat Akcakaya, Jeannie S Huang, R de Sa Virginia

引用次数: 0

Personalizing Mobile Fitness Apps using Reinforcement Learning. 利用强化学习实现移动健身应用程序的个性化。

CEUR workshop proceedings Pub Date : 2018-03-07

Mo Zhou, Yonatan Mintz, Yoshimi Fukuoka, Ken Goldberg, Elena Flowers, Philip Kaminsky, Alejandro Castillejo, Anil Aswani

引用次数: 0

Towards Automatic Generation of Portions of Scientific Papers for Large Multi-Institutional Collaborations Based on Semantic Metadata. 基于语义元数据的大型多机构协作科学论文部分自动生成研究。

CEUR workshop proceedings Pub Date : 2017-10-01

MiHyun Jang, Tejal Patted, Yolanda Gil, Daniel Garijo, Varun Ratnakar, Jie Ji, Prince Wang, Aggie McMahon, Paul M Thompson, Neda Jahanshad

引用次数: 0

UArizona at the CLEF eRisk 2017 Pilot Task: Linear and Recurrent Models for Early Depression Detection. 2017年CLEF风险试点任务:早期抑郁症检测的线性和循环模型。

CEUR workshop proceedings Pub Date : 2017-09-01 Epub Date: 2017-07-13

Farig Sadeque, Dongfang Xu, Steven Bethard

引用次数: 0

Clinical Information Extraction at the CLEF eHealth Evaluation lab 2016. CLEF健康评估实验室临床信息提取2016。

CEUR workshop proceedings Pub Date : 2016-09-01

Aurélie Névéol, K Bretonnel Cohen, Cyril Grouin, Thierry Hamon, Thomas Lavergne, Liadh Kelly, Lorraine Goeuriot, Grégoire Rey, Aude Robert, Xavier Tannier, Pierre Zweigenbaum

{"title":"Clinical Information Extraction at the CLEF eHealth Evaluation lab 2016.","authors":"Aurélie Névéol, K Bretonnel Cohen, Cyril Grouin, Thierry Hamon, Thomas Lavergne, Liadh Kelly, Lorraine Goeuriot, Grégoire Rey, Aude Robert, Xavier Tannier, Pierre Zweigenbaum","doi":"","DOIUrl":"","url":null,"abstract":"This paper reports on Task 2 of the 2016 CLEF eHealth evaluation lab which extended the previous information extraction tasks of ShARe/CLEF eHealth evaluation labs. The task continued with named entity recognition and normalization in French narratives, as offered in CLEF eHealth 2015. Named entity recognition involved ten types of entities including disorders that were defined according to Semantic Groups in the Unified Medical Language System® (UMLS®), which was also used for normalizing the entities. In addition, we introduced a large-scale classification task in French death certificates, which consisted of extracting causes of death as coded in the International Classification of Diseases, tenth revision (ICD10). Participant systems were evaluated against a blind reference standard of 832 titles of scientific articles indexed in MEDLINE, 4 drug monographs published by the European Medicines Agency (EMEA) and 27,850 death certificates using Precision, Recall and F-measure. In total, seven teams participated, including five in the entity recognition and normalization task, and five in the death certificate coding task. Three teams submitted their systems to our newly offered reproducibility track. For entity recognition, the highest performance was achieved on the EMEA corpus, with an overall F-measure of 0.702 for plain entities recognition and 0.529 for normalized entity recognition. For entity normalization, the highest performance was achieved on the MEDLINE corpus, with an overall F-measure of 0.552. For death certificate coding, the highest performance was 0.848 F-measure.","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"1609 ","pages":"28-42"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5756095/pdf/nihms921614.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35715159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying Missing Hierarchical Relations in SNOMED CT from Logical Definitions Based on the Lexical Features of Concept Names. 基于概念名称词法特征的逻辑定义中缺失层次关系识别。

CEUR workshop proceedings Pub Date : 2016-08-01

Olivier Bodenreider

引用次数: 0