2015 IEEE 28th International Symposium on Computer-Based Medical Systems最新文献

Ontology Network Definition for Motivational Interviewing Learning Driven by Semantic Context-Awareness 语义上下文感知驱动动机性访谈学习的本体网络定义

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.22

Vinícius Maran, J. Oliveira, R. Pietrobon, Iara Augustin

引用次数: 10

Blockwise Classification of Lung Patterns in Unsegmented CT Images 未分割CT图像中肺模式的块分类

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.32

Luiza Dri Bagesteiro, L. F. Oliveira, Daniel Weingaertner

引用次数: 6

Regional Anaesthesia Simulator and Assistant (RASimAs): Medical Image Processing Supporting Anaesthesiologists in Training and Performance of Local Blocks 区域麻醉模拟器和助理(rasima):医学图像处理支持麻醉师的培训和局部阻滞的表现

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.61

T. Deserno, J. E. E. D. Oliveira, O. Grottke

引用次数: 15

Mobile Crowd Sensing in Clinical and Psychological Trials -- A Case Study 移动人群感知在临床和心理试验中的应用——一个案例研究

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.26

R. Pryss, M. Reichert, Jochen Herrmann, B. Langguth, W. Schlee

{"title":"Mobile Crowd Sensing in Clinical and Psychological Trials -- A Case Study","authors":"R. Pryss, M. Reichert, Jochen Herrmann, B. Langguth, W. Schlee","doi":"10.1109/CBMS.2015.26","DOIUrl":"https://doi.org/10.1109/CBMS.2015.26","url":null,"abstract":"Many highly prevalent diseases (e.g., tinnitus, migraine, chronic pain) are difficult to treat and universally effective treatments are missing. Available treatments are only effective in patient subgroups, i.e., medical doctors and patients have to figure out which therapy might be helpful in the patient's situation. Sufficiently large and qualitative longitudinal data sets, however, would be desirable to facilitate evidence-based treatment decisions for individual patients. On one hand, traditional sensing techniques (i.e., clinical trials) have many merits enabling evidence-based medicine. On the other, they have inherent limitations. First, clinical trials are very cost- and labour-intensive. Second, the traditional approach aims at reducing ecological heterogeneity to enable the investigation of homogeneous subsamples. Recently, a new paradigm emerged that offers promising perspectives for collecting large amounts of longitudinal patient data -- Mobile Crowd Sensing. By utilizing smart mobile devices of a large number of patients, health information can be gathered from large patient collections as well as at many different time points and in various real life environmental situations. In the Track Your Tinnitus project, we implemented such a mobile crowd sensing platform to reveal new medical aspects about tinnitus with a particular focus on the variability of tinnitus over time depending on the environmental situation. In this paper, the current project status as well as first lessons learned from running the mobile application for twelve months are presented. In turn, the lessons learned are discussed in the context of the new perspectives offered by mobile crowd sensing in the medical field.","PeriodicalId":164356,"journal":{"name":"2015 IEEE 28th International Symposium on Computer-Based Medical Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126831408","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: 73

Obstructive Sleep Apnea Diagnosis: The Bayesian Network Model Revisited 阻塞性睡眠呼吸暂停诊断:重新审视贝叶斯网络模型

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.47

P. Rodrigues, D. F. Santos, Liliana Leite

{"title":"Obstructive Sleep Apnea Diagnosis: The Bayesian Network Model Revisited","authors":"P. Rodrigues, D. F. Santos, Liliana Leite","doi":"10.1109/CBMS.2015.47","DOIUrl":"https://doi.org/10.1109/CBMS.2015.47","url":null,"abstract":"Obstructive Sleep Apnea (OSA) is a disease that affects approximately 4% of men and 2% of women worldwide but is still underestimated and underdiagnosed. The standard method for assessing this index, and therefore defining the OSA diagnosis, is polysomnography (PSG). Previous work developed relevant Bayesian network models but those were based only on variables univariatedly associated with the outcome, yielding a bias on the possible knowledge representation of the models. The aim of this work was to develop and validate new Bayesian network decision support models that could be used during sleep consult to assess the need for PSG. Bayesian models were developed using a) expert opinion, b) hill-climbing, c) naïve Bayes and d) TAN structures. Resulting models validity was assessed with in-sample AUC and stratified cross-validation, also comparing with previously published model. Overall, models achieved good discriminative power (AUC>70%) and validity (measures consistently above 70%). Main conclusions are a) the need to integrate a wider range of variables in the final models and b) the support of using Bayesian networks in the diagnosis of obstructive sleep apnea.","PeriodicalId":164356,"journal":{"name":"2015 IEEE 28th International Symposium on Computer-Based Medical Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122682414","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

Interactive Segmentation Relabeling for Classification of Whole-Slide Histopathology Imagery 全片组织病理图像分类的交互式分割重标记

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.89

Anoop Haridas, F. Bunyak, K. Palaniappan

{"title":"Interactive Segmentation Relabeling for Classification of Whole-Slide Histopathology Imagery","authors":"Anoop Haridas, F. Bunyak, K. Palaniappan","doi":"10.1109/CBMS.2015.89","DOIUrl":"https://doi.org/10.1109/CBMS.2015.89","url":null,"abstract":"Collecting ground-truth or gold standard annotations from expert pathologists for developing histopathology analytic algorithms and computer-aided diagnosis for cancer grading is an expensive and time consuming process. Efficient visualization and annotation tools are needed to enable ground-truthing large whole-slide imagery. KOLAM is our scalable, cross-platform framework for interactive visualization of 2D, 2D+t and 3D imagery of high spatial, temporal and spectral resolution. In the current work KOLAM has been extended to support rapid interactive labelling and correction of automatic image classifier-based region labels of the tissue microenvironment by pathologists. Besides annotating regions-of-interest (ROIs), KOLAM enables extraction of the corresponding large polygonal image subregions for input into automatic segmentation algorithms, single-click region label reassignment and maintaining hierarchical image subregions. Experience indicates that clinicians prefer simple-to-use interfaces that support rapid labelling of large image regions with minimal effort. The incorporation of easy-to-use tissue annotation features in KOLAM makes it an attractive candidate for integration within a multi-stage histopathology image analysis pipeline supporting assisted segmentation and labelling to improve whole-slide imagery (WSI) analytics.","PeriodicalId":164356,"journal":{"name":"2015 IEEE 28th International Symposium on Computer-Based Medical Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114543775","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

A Serious Game for Improving Community-Based Prevention of Neglected Diseases 改善社区被忽视疾病预防的严肃游戏

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.17

M. Masoodian, S. Luz, Manuel Cesario, R. R. Cesario, Bill Rogers, Diones A. Borges

引用次数: 1

Chest X-ray Image View Classification 胸部x线图像查看分类

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.49

Z. Xue, D. You, S. Candemir, Stefan Jaeger, Sameer Kiran Antani, L. Long, G. Thoma

引用次数: 46

Predicting Cardiopulmonary Response to Incremental Exercise Test 预测心肺对增量运动试验的反应

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.60

Elena Baralis, T. Cerquitelli, S. Chiusano, A. Giordano, A. Mezzani, D. Susta, Xin Xiao

引用次数: 5

Traceability and Provenance in Big Data Medical Systems 大数据医疗系统中的溯源与溯源

2015 IEEE 28th International Symposium on Computer-Based Medical Systems Pub Date : 2015-06-22 DOI: 10.1109/CBMS.2015.10

R. McClatchey, Jetendr Shamdasani, A. Branson, K. Munir, Z. Kovács, G. Frisoni

引用次数: 9