2014 IEEE 27th International Symposium on Computer-Based Medical Systems最新文献_第9页

Privacy-Aware Large-Scale Virological and Epidemiological Data Monitoring 具有隐私意识的大规模病毒学和流行病学数据监测

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.89

C. Patsakis, Michael Clear, Paul Laird, Athanasios Zigomitros, Mélanie Bouroche

引用次数: 3

Design and Development of a Gastrointestinal Simulator System with Software That Allows to Find the Anatomical Location and a Flexible Endoscope Emulator 胃肠模拟器系统的设计与开发，包含可查找解剖位置的软件和灵活的内窥镜模拟器

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.117

E. Moncisvalles, D. Lorias, A. Minor, J. Villalobos

引用次数: 0

Using Participant Similarity for the Classification of Epidemiological Data on Hepatic Steatosis 利用参与者相似性对肝脂肪变性流行病学资料进行分类

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.28

Tommy Hielscher, M. Spiliopoulou, H. Völzke, J. Kühn

引用次数: 21

Segmentation of Small Bowel Tumors in Wireless Capsule Endoscopy Using Level Set Method 水平集法在无线胶囊内镜下小肠肿瘤分割中的应用

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.140

M. Alizadeh, H. Soltanian-Zadeh, O. H. Maghsoudi

引用次数: 20

Automated Classification of Computer-Based Medical Device Recalls: An Application of Natural Language Processing and Statistical Learning 基于计算机的医疗器械召回自动分类:自然语言处理和统计学习的应用

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.134

H. Alemzadeh, Raymond Hoagland, Z. Kalbarczyk, R. Iyer

引用次数: 0

New Image Analysis Technique for Quantitative Longitudinal Assessment of Lung Pathology on CT in Infected Rhesus Macaques 感染恒河猴肺部CT病理定量纵向评估的新图像分析技术

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.59

J. Solomon, D. Douglas, Reed F. Johnson, D. Hammoud

引用次数: 12

Decoding Movements from Human Deep Brain Local Field Potentials Using Radial Basis Function Neural Network 利用径向基函数神经网络解码人脑深部局部场电位的运动

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.77

M. S. Islam, Muhammad S. Khan, Hai Deng, K. Mamun

{"title":"Decoding Movements from Human Deep Brain Local Field Potentials Using Radial Basis Function Neural Network","authors":"M. S. Islam, Muhammad S. Khan, Hai Deng, K. Mamun","doi":"10.1109/CBMS.2014.77","DOIUrl":"https://doi.org/10.1109/CBMS.2014.77","url":null,"abstract":"Research on neural process is fundamental to understand neurodegenerative disorders and develop its interventions. This also enhances the development of brain machine interfaces to assist neurologically impaired human and rehabilitation. This study aimed to decode deep brain local field potentials (LFPs) related to voluntary movement activities and its forthcoming laterality, left or right sided visually cued movements. The frequency related components of local field potentials from the sub thalamic nucleus (STN) were decomposed by time scale domain using wavelet packet transform (WPT). In each frequency component, event related instantaneous power was considered as features for decoding. Decoding of movement (Event vs. Rest) and its sequential laterality (Left vs. Right) were performed using radial basis function neural network (RBFNN). The average classification accuracy achieved 85.93% for distinguishing movement from the rest, while laterality discrimination, the accuracy achieved 70.81% with 10 fold cross validation. The RBFNN classifier successfully managed to achieve decoding accuracy better than the chance level during movement and its laterality for all subjects.","PeriodicalId":398710,"journal":{"name":"2014 IEEE 27th International Symposium on Computer-Based Medical Systems","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130248099","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

A Domain Specific Ontology Authoring Environment for a Clinical Documentation System 面向临床文档系统的领域特定本体创作环境

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.84

M. Horridge, S. Brandt, B. Parsia, A. Rector

引用次数: 6

Social Network Analysis to Delineate Interaction Patterns That Predict Weight Loss Performance 社会网络分析，描绘互动模式，预测减肥业绩

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.67

T. Chomutare, A. Xu, M. Sriram Iyengar

{"title":"Social Network Analysis to Delineate Interaction Patterns That Predict Weight Loss Performance","authors":"T. Chomutare, A. Xu, M. Sriram Iyengar","doi":"10.1109/CBMS.2014.67","DOIUrl":"https://doi.org/10.1109/CBMS.2014.67","url":null,"abstract":"Social media is an interesting, relatively new topic in health and self-management, which is generating enormous amounts of data, but little is yet known about its effect on the health of participants. The goal of this study is to determine online interaction behaviours that predict weight loss performance. The problem is modelled as a binomial classification task for predicting whether a patient would lose significant weight, based on analysis of two obesity online communities. An expansion-reduction method was developed for the patient feature vector, where the expansion is based on concatenating network structure features and the reduction is based on feature subset selection. Further, empirical evaluation of classifiers was done on the datasets, before and after the expansion. Based on feature subset selection, centrality measures such as degree and betweenness were more predictive than basic demographic features. Top performers, compared with bottom performers, were significantly more active online and connected to more than one sub-community (at 95% CI and p<;.05). In terms of classification, we found naive Bayes and decision tree methods had superior performance on the datasets, drastically reducing the false positive (FP) rate in some instances, and reaching a maximum F-score of 0.977, precision of 0.978 and AUC of 0.996. Current findings are consistent with previous reports that amount of online engagement correlates with weight loss, but our findings speak further to the types of engagement that yield best results.","PeriodicalId":398710,"journal":{"name":"2014 IEEE 27th International Symposium on Computer-Based Medical Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114276841","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: 8

An Information Theoretic Approach via IJM to Segmenting MR Images with MS Lesions 基于IJM的MR图像与MS病灶分割的信息理论方法

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.130

J. Hill, B. Nutter, S. Mitra

引用次数: 5