2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)最新文献_第4页

A Tool for Optimizing De-identified Health Data for Use in Statistical Classification 优化用于统计分类的去识别健康数据的工具

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.105

F. Prasser, J. Eicher, Raffael Bild, Helmut Spengler, K. Kuhn

{"title":"A Tool for Optimizing De-identified Health Data for Use in Statistical Classification","authors":"F. Prasser, J. Eicher, Raffael Bild, Helmut Spengler, K. Kuhn","doi":"10.1109/CBMS.2017.105","DOIUrl":"https://doi.org/10.1109/CBMS.2017.105","url":null,"abstract":"When individual-level health data is shared in biomedical research the privacy of patients and probands must be protected. This is typically achieved with methods of data de-identification, which transform data in such a way that formal guarantees about the degree of protection from re-identification can be provided. In the process it is important to minimize loss of information to ensure that the resulting data is useful. A typical use case is the creation of predictive models for knowledge discovery and decision support, e.g. to infer diagnoses or to predict outcomes of therapies. A variety of methods have been developed which can be used to build robust statistical classifiers from de-identified data. However, they have not been tuned for practical use and they have not been implemented into mature software tools. To bridge this gap, we have extended ARX, an open source anonymization tool for health data, with several new features. We have implemented a method for optimizing the suitability of de-identified data for building statistical classifiers and a method for assessing the performance of classifiers built from de-identified data. All methods are accessible via a comprehensive graphical user interface. We have used our implementation to create logistic regression models from a patient discharge dataset for predicting the costs of hospital stays. The results show that our approach enables the creation of privacy-preserving classifiers with optimal prediction accuracy.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127748943","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: 20

BREATH: Heat Maps Assisting the Detection of Abnormal Lung Regions in CT Scans 呼吸:在CT扫描中帮助检测异常肺区域的热图

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.82

M. Cazzolato, L. C. Scabora, Alceu Ferraz Costa, Marcos Roberto Nesso Junior, Luis Fernando Milano Oliveira, D. S. Kaster, C. Traina, A. Traina

{"title":"BREATH: Heat Maps Assisting the Detection of Abnormal Lung Regions in CT Scans","authors":"M. Cazzolato, L. C. Scabora, Alceu Ferraz Costa, Marcos Roberto Nesso Junior, Luis Fernando Milano Oliveira, D. S. Kaster, C. Traina, A. Traina","doi":"10.1109/CBMS.2017.82","DOIUrl":"https://doi.org/10.1109/CBMS.2017.82","url":null,"abstract":"Computed Tomography (CT) scans are often employed to diagnose lung diseases, as abnormal tissue regions may indicate whether proper treatment is required. However, detecting specific regions containing abnormalities in a CT scan demands time and effort of specialists. Moreover, different parts of a single lung image may present both normal and abnormal characteristics, what makes inaccurate the classification of a single lung as healthy (normal) or not. In this paper we propose the BREATH method, capable of detecting abnormalities in lung tissue regions, highlighting them by means of a heat map visualization. The method starts by segmenting lung tissues using a superpixel-based approach, followed by the training of a statistical model to represent normal tissues and, finally, the generation of a heat map showing abnormal regions that require attention from the physicians. We validated our statistical model using a dataset with 246 lung CT scans, where 40 are healthy and the remaining present varying diseases. Experimental results show that BREATH is accurate for lung segmentation with F-Measure of up to 0.99. The statistical modeling of healthy and abnormal lung regions has shown almost no overlap, and the detection of superpixels containing abnormalities presented precision values higher than 86%, for all values of recall. These values support our claim that the heat map representation of BREATH for the abnormal detection can be used as an intuitive method to assist physicians during the diagnosis.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115797739","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: 2

ICE: Interactive Classification Rule Exploration on Epidemiological Data 流行病学数据的交互式分类规则探索

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.127

Miro Schleicher, T. Ittermann, Uli Niemann, H. Völzke, M. Spiliopoulou

引用次数: 2

Brain Image and Lesions Registration and 3D Reconstruction in Dicom MRI Images Dicom MRI图像中的脑图像和病变配准及三维重建

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.53

C. Loizou, Christos Papacharalambous, Giorgos Samaras, E. Kyriacou, T. Kasparis, M. Pantziaris, E. Eracleous, C. Pattichis

{"title":"Brain Image and Lesions Registration and 3D Reconstruction in Dicom MRI Images","authors":"C. Loizou, Christos Papacharalambous, Giorgos Samaras, E. Kyriacou, T. Kasparis, M. Pantziaris, E. Eracleous, C. Pattichis","doi":"10.1109/CBMS.2017.53","DOIUrl":"https://doi.org/10.1109/CBMS.2017.53","url":null,"abstract":"During a human brain MRI acquisition the resulting image is formed out of 2D slices. The slices must then be aligned and reconstructed to provide a 3-dimensional (3D) visualization of the brain volume. We propose in this work, an integrated system for the register ion and 3D reconstruction of DICOM MRI images and lesions of the brain acquired from multiple sclerosis (MS) subjects at two different time intervals (time 0 (T0) and time 1 (T1)). The system facilitates the follow up of the MS disease development and will aid the doctor to accurately manage the follow up of the disease. It involves a 6-stage analysis (preprocessing, lesion segmentation, registration, 3D reconstruction, volume estimation and method evaluation), as well as module quantitative evaluation of the method. The system was evaluated based on one MRI phantom and one DICOM MRI image of the brain. The accuracy of the proposed registration and reconstruction (- / -) method was 78.5%/97.2% and 95.4%/95.8% for the phantom and the MRI images respectively. These preliminary results provide evidence that the proposed system could be applied in future in the clinical practice.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125251643","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

Comparing the Quality of Numeracy Assessment Methods in Healthcare 医疗卫生计算能力评估方法的质量比较

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.90

M. Omidbakhsh, O. Ormandjieva

引用次数: 0

Visual Localization of Wireless Capsule Endoscopes Aided by Artificial Neural Networks 基于人工神经网络的无线胶囊内窥镜视觉定位

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.67

George Dimas, Dimitrios K. Iakovidis, G. Ciuti, A. Karargyris, Anastasios Koulaouzidis

{"title":"Visual Localization of Wireless Capsule Endoscopes Aided by Artificial Neural Networks","authors":"George Dimas, Dimitrios K. Iakovidis, G. Ciuti, A. Karargyris, Anastasios Koulaouzidis","doi":"10.1109/CBMS.2017.67","DOIUrl":"https://doi.org/10.1109/CBMS.2017.67","url":null,"abstract":"Various modalities are used for the examination of the gastrointestinal (GI) tract. One such modality is Wireless Capsule Endoscopy (WCE), a non-invasive technique which consists of a swallowable color camera that enables the detection of GI pathology with only minimal patient discomfort. Currently, tracking of the capsule position is estimated in the 3D abdominal space, using radio-frequency (RF) triangulation. The RF triangulation technique, however, does not provide sufficient information about the location of the capsule along the GI lumen, and consequently, the localization of any possible abnormality. Recently, we proposed a geometric visual odometry (VO) method for the localization of the capsule in the GI lumen. In this paper, we extend this state-of-art method by exploiting an artificial neural network (ANN) to augment the geometric method and achieve higher localization accuracy. The results of this novel approach are validated with an in-vitro experiment that provides ground truth information about the location of the capsule. The mean absolute error obtained, for a distance of 19.6cm, is 0.79±0.51cm.","PeriodicalId":141105,"journal":{"name":"2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121704566","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: 12

Using Affective Computing to Automatically Adapt Serious Games for Rehabilitation 使用情感计算自动适应康复的严肃游戏

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.89

R. V. Aranha, Leonardo Souza Silva, M. Chaim, Fátima L. S. Nunes

引用次数: 13

iManageCancer: Developing a Platform for Empowering Patients and Strengthening Self-Management in Cancer Diseases iManageCancer:开发癌症疾病患者赋权和自我管理平台

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI: 10.1109/CBMS.2017.62

H. Kondylakis, A. Bucur, F. Dong, C. Renzi, A. Manfrinati, N. Graf, S. Hoffman, L. Koumakis, G. Pravettoni, K. Marias, M. Tsiknakis, Stephan Kiefer

引用次数: 51

Towards a Conceptual Framework Fostering Process Comprehension in Healthcare 构建促进医疗保健过程理解的概念框架

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.70

Michael Zimoch, R. Pryss, T. Probst, W. Schlee, M. Reichert

引用次数: 2

The Application of Neuromuscular Electrical Stimulation (NMES) Technologies in Cancer Care 神经肌肉电刺激(NMES)技术在癌症治疗中的应用

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-01 DOI: 10.1109/CBMS.2017.95

Dominic O’Connor, B. Caulfield

引用次数: 0