2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)最新文献_第5页

Computational Analysis of BRCA1 Mutations in Pediatric Patients with Malignancies and Their Mothers 儿科恶性肿瘤患者及其母亲BRCA1突变的计算分析

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.111

G. Lambrou, I. Barbounaki, F. Tzortzatou-Stathopoulou, O. Petropoulou, P. Katrakazas, D. Iliopoulou, D. Koutsouris

{"title":"Computational Analysis of BRCA1 Mutations in Pediatric Patients with Malignancies and Their Mothers","authors":"G. Lambrou, I. Barbounaki, F. Tzortzatou-Stathopoulou, O. Petropoulou, P. Katrakazas, D. Iliopoulou, D. Koutsouris","doi":"10.1109/CBMS.2017.111","DOIUrl":"https://doi.org/10.1109/CBMS.2017.111","url":null,"abstract":"Breast and ovarian cancers are the most prevalent type of malignancies amongst women. Similar incidence appear in childhood malignancies, where the basic ontogenetic mechanisms still remain to be elucidated. Such approaches, of relating mothers cancer mutations with the prevalence of childhood cancer in their offspring could prove useful in the prognosis, early detection and therapy of childhood malignancies. The aim of the present study was to use computational and bioinformatics tools to investigate the incidence of mutations in mothers with children suffering from neoplasms. Genes were examined for mutations and in particular, those were BRCA1, RAS family genes, TP53 and FLT3. Mutations were initially detected using PCR and multiplex Polymerase Chain Reaction (PCR) methodologies. Gene expression was detected using quantitative Reverse Transcription PCR (qRT-PCR) methodologies and results have been confirmed with the sequencing method. Following experimental analysis, bioinformatics analyses have been performed. In the case of positive identification of mutations, molecular modelling was used in order to study the effects of the mutations on the BRCA protein and subsequent effects on binding to BARD1, a signaling molecule down-stream of BRCA1, which participates in DNA repair pathways. Concluding, it appeared that the presence of a mutation in the aforementioned genes is not adequate for the disease to progress, yet it can be considered as a serious factor for disease progression. Thus, it appears that this phenomenon is of extreme interest and it should be further investigated in a larger patient cohort.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122580267","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: 1

Visual Versus Kinesthetic Motor Imagery for BCI Control of Robotic Arms (Mercury 2.0) 机械臂BCI控制的视觉与动觉运动意象(Mercury 2.0)

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.34

G. Arfaras, A. Athanasiou, N. Pandria, K. R. Kavazidi, Panagiotis Kartsidis, A. Astaras, P. Bamidis

{"title":"Visual Versus Kinesthetic Motor Imagery for BCI Control of Robotic Arms (Mercury 2.0)","authors":"G. Arfaras, A. Athanasiou, N. Pandria, K. R. Kavazidi, Panagiotis Kartsidis, A. Astaras, P. Bamidis","doi":"10.1109/CBMS.2017.34","DOIUrl":"https://doi.org/10.1109/CBMS.2017.34","url":null,"abstract":"Motor Imagery (MI), the mental execution of an action, is widely applied as a control modality for electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). Different approaches to MI have been implemented, namely visual observation (VMI) or kinesthetic rehearsal (KMI) of movements. Although differences in brain activity during VMI or KMI have been studied, no investigation with regards to their suitability for BCI applications has been made. The choice of MI approach could affect individual performance during BCI control, especially for off-the-shelf BCI systems, where ease of use and fast reliable results is the target. Whether for healthy individuals or clinical applications, if such systems are expected to reach consumer maturity, best practices for their use should be investigated. We designed a study to compare VMI and KMI as control modalities of an off-the-shelf EEG-BCI system. 30 healthy individuals (18 male, 12 female) participated in the study, operating two house-developed robotic arms (Mercury 2.0) using an Emotiv EPOC EEG-BCI. They were asked to use first VMI and then KMI to achieve BCI control and we compared the training and success rates. In our study, KMI achieved higher skill percentages during imagery training but VMI achieved higher success rates during BCI control of both robotic arms. Nonetheless, observed differences did not exceed significance thresholds. Individual differences could play a major role in MI performance and should be taken into account when choosing which modality to train for the use of a BCI system.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122892756","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 IT Platform Enabling Remote Therapeutic Interventions 实现远程治疗干预的IT平台

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.78

Marc Schickler, R. Pryss, Michael Stach, Johannes Schobel, W. Schlee, T. Probst, B. Langguth, M. Reichert

{"title":"An IT Platform Enabling Remote Therapeutic Interventions","authors":"Marc Schickler, R. Pryss, Michael Stach, Johannes Schobel, W. Schlee, T. Probst, B. Langguth, M. Reichert","doi":"10.1109/CBMS.2017.78","DOIUrl":"https://doi.org/10.1109/CBMS.2017.78","url":null,"abstract":"The development of information systems, which support homework in the context of therapeutic interventions, has not been sufficiently addressed so far. However, both therapists and patients crave for a mobile assistance managing complex homework procedures. For example, smart mobile devices can automatically inform therapists about corresbonding outcomes, giving them the opportunity to timely adjust homework if required. When realizing information systems that integrate smart mobile devices, the common procedure of therapeutic interventions in general and homework in particular must be carefully captured by the system. Therefore, relevant requirements were elicitated in real-world projects. Based on these requirements, we realized the Albatros platform enabling therapists to manage therapeutic interventions remotely. Using the platform, homework can be created with a web-based component and be performed by patients with the help of smart mobile devices. In this paper, elicitated requirements for realizing the platform as well as its features and architecture are presented. Altogether, the Albatros platform enables therapists as well as patients to manage therapeutic interventions and homework more efficiently.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129083389","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: 16

Z-Box Merging: Ultra-Fast Computation of Fractal Dimension and Lacunarity Z-Box合并:分形维数和空隙度的超快速计算

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.121

John Nikolaides, E. Aifantis

引用次数: 1

Personalization of Infectious Disease Risk Prediction: Towards Automatic Generation of a Bayesian Network 传染病风险预测的个性化:迈向贝叶斯网络的自动生成

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.24

R. Vinarti, L. Hederman

{"title":"Personalization of Infectious Disease Risk Prediction: Towards Automatic Generation of a Bayesian Network","authors":"R. Vinarti, L. Hederman","doi":"10.1109/CBMS.2017.24","DOIUrl":"https://doi.org/10.1109/CBMS.2017.24","url":null,"abstract":"Infectious diseases are a major cause of human morbidity, but most are avoidable. An accurate and personalized risk prediction is expected to alert people to the risk of getting exposed to infectious diseases. However, as data and knowledge in the epidemiology and infectious diseases field becomes available, an updateable risk prediction model is needed. The objectives of this article are (1) to describe the mechanisms for generating a Bayesian Network (BN), as risk prediction model, from a knowledge-base, and (2) to examine the accuracy of the prediction result. The research in this paper started by encoding declarative knowledge from the Atlas of Human Infectious Diseases into an Infectious Disease Risk Ontology. Automatic generation of a BN from this knowledge uses two tools (1) a Rule Converter generates a BN structure from the ontology (2) a Joint & Marginal Probability Supplier tool populates the BN with probabilities. These tools allow the BN to be recreated automatically whenever knowledge and data changes. In a runtime phase, a third tool, the Context Collector, captures facts given by the client and consequent environmental context. This paper introduces these tools and evaluates the effectiveness of the resulting BN for a single infectious disease, Anthrax. We have compared conditional probabilities predicted by our BN against incidence estimated from real patient visit records. Experiments explored the role of different context data in prediction accuracy. The results suggest that building a BN from an ontology is feasible. The experiments also show that more context results in better risk prediction.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126664727","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

Trust, Ethics and Access: Challenges in Studying the Work of Multi-disciplinary Medical Teams 信任、道德和获取:研究多学科医疗团队工作的挑战

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.150

B. Kane, S. Luz

引用次数: 0

Conditional Entropy Based Retrieval Model in Patient-Carer Conversational Cases 基于条件熵的医患对话案例检索模型

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.145

M. Pavlidou, Antonis Billis, N. D. Hasanagas, C. Bratsas, Ioannis Antoniou, P. Bamidis

引用次数: 3

The Effect of Mammogram Preprocessing on Microcalcification Detection with Convolutional Neural Networks 乳房x光片预处理对卷积神经网络微钙化检测的影响

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.29

Agnese Marchesi, A. Bria, C. Marrocco, M. Molinara, J. Mordang, F. Tortorella, N. Karssemeijer

{"title":"The Effect of Mammogram Preprocessing on Microcalcification Detection with Convolutional Neural Networks","authors":"Agnese Marchesi, A. Bria, C. Marrocco, M. Molinara, J. Mordang, F. Tortorella, N. Karssemeijer","doi":"10.1109/CBMS.2017.29","DOIUrl":"https://doi.org/10.1109/CBMS.2017.29","url":null,"abstract":"Microcalcifications are an early mammographic indicator of breast cancer. To assist screening radiologists in reading mammograms, machine learning techniques have been developed for the automated detection of microcalcifications. In the last few years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision and medical image analysis applications. A key step in CNN-based detection is image preprocessing, including brightness and contrast variations. In this work, we investigate the influence of preprocessing of digital mammograms on the microcalcification detection performance of two CNNs inspired by the popular AlexNet and VGGnet. We tested two preprocessing methods commonly applied to unprocessed raw digital mammograms: (i) the logarithmic transformation adopted by different manufacturers for the presentation of the image to the radiologists; and (ii) the square-root of image intensity that stabilizes the intensity-dependent noise present in the mammogram. Experiments were performed on 1,066 mammograms acquired with GE Senographe systems. Both preprocessing methods yielded statistically significantly better microcalcification detection performance. Results of the square-root transform were superior to those obtained with the log transform.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130996517","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: 9

Predicting Sepsis Biomarker Progression under Therapy 预测治疗下败血症生物标志物进展

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.16

Ivan Stojkovic, Z. Obradovic

引用次数: 6

Pattern-Based Statechart Modeling Approach for Medical Best Practice Guidelines - A Case Study 医疗最佳实践指南的基于模式的状态图建模方法-一个案例研究

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.14

Chunhui Guo, Zhicheng Fu, Shangping Ren, Yu Jiang, M. Rahmaniheris, L. Sha

引用次数: 9