CEUR workshop proceedings最新文献_第3页

Explaining Deep Classification of Time-Series Data with Learned Prototypes. 用学习原型解释时间序列数据的深度分类。

CEUR workshop proceedings Pub Date : 2019-08-01

Alan H Gee, Diego Garcia-Olano, Joydeep Ghosh, David Paydarfar

{"title":"Explaining Deep Classification of Time-Series Data with Learned Prototypes.","authors":"Alan H Gee, Diego Garcia-Olano, Joydeep Ghosh, David Paydarfar","doi":"","DOIUrl":"","url":null,"abstract":"The emergence of deep learning networks raises a need for explainable AI so that users and domain experts can be confident applying them to high-risk decisions. In this paper, we leverage data from the latent space induced by deep learning models to learn stereotypical representations or \"prototypes\" during training to elucidate the algorithmic decision-making process. We study how leveraging prototypes effect classification decisions of two dimensional time-series data in a few different settings: (1) electrocardiogram (ECG) waveforms to detect clinical bradycardia, a slowing of heart rate, in preterm infants, (2) respiration waveforms to detect apnea of prematurity, and (3) audio waveforms to classify spoken digits. We improve upon existing models by optimizing for increased prototype diversity and robustness, visualize how these prototypes in the latent space are used by the model to distinguish classes, and show that prototypes are capable of learning features on two dimensional time-series data to produce explainable insights during classification tasks. We show that the prototypes are capable of learning real-world features - bradycardia in ECG, apnea in respiration, and articulation in speech - as well as features within sub-classes. Our novel work leverages learned prototypical framework on two dimensional time-series data to produce explainable insights during classification tasks.","PeriodicalId":72554,"journal":{"name":"CEUR workshop proceedings","volume":"2429 ","pages":"15-22"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050893/pdf/nihms-1668684.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38884015","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

The New SNOMED CT International Medicinal Product Model. 新型SNOMED CT国际医药产品模型。

CEUR workshop proceedings Pub Date : 2018-12-01

Olivier Bodenreider, Julie James

引用次数: 0

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