19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)最新文献_第3页

Lessons Learned from MammoGrid for Integrated Biomedical Solutions 综合生物医学解决方案的乳房x线网经验教训

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.109

R. McClatchey, D. Manset, T. Solomonides

引用次数: 10

Using Visual Interpretation of Small Ensembles in Microarray Analysis 在微阵列分析中使用小集成的视觉解释

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.169

G. Štiglic, M. Mertik, V. Podgorelec, P. Kokol

{"title":"Using Visual Interpretation of Small Ensembles in Microarray Analysis","authors":"G. Štiglic, M. Mertik, V. Podgorelec, P. Kokol","doi":"10.1109/CBMS.2006.169","DOIUrl":"https://doi.org/10.1109/CBMS.2006.169","url":null,"abstract":"Many different classification models and techniques have been employed on gene expression data. These computational methods are in rapid and continuous evolution and there is no clear consensus on which methods are best to cope with the complex microarray data analysis. Currently ensembles of classifiers are regarded as one of the best classification techniques as they can achieve excellent classification accuracy in comparison to single classifiers methods. One of their main drawbacks is their incomprehensibility. This paper addresses the important issue of the tradeoff between accuracy and comprehensibility when building ensembles and proposes a novel visual technique for interactive interpretation of the knowledge from the small ensembles consisting of only a few decision trees. This way we can achieve better accuracy compared to single classifier, but still maintain a certain level of comprehensibility in small ensembles. The results show that our small ensembles outperform the single classifiers and still retain comprehensibility. Our study also points out that in order to take advantage of our proposed method we need more effective small ensemble building techniques","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114313112","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: 15

An Aid System to the Medical Diagnosis of Patients with Neurological Problems 神经系统疾病患者医学诊断辅助系统

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.36

E. Quirino, F. Paraguaçu, G.M.C. Souza

引用次数: 4

Clustering Fetal Heart Rate Tracings by Compression 压缩聚类胎儿心率追踪

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.68

C. Costa-Santos, J. Bernardes, P. Vitányi, L. Antunes

引用次数: 62

Proteome Profiling without Selection Bias 无选择偏倚的蛋白质组分析

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.134

A. Barla, Bettina Irler, S. Merler, Giuseppe Jurman, S. Paoli, Cesare Furlanello

引用次数: 8

Using Ontology Visualization to Coordinate Cross-species Functional Annotation for Human Disease Genes 利用本体可视化协调人类疾病基因的跨物种功能标注

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.165

M. Dolan, J. Blake

引用次数: 7

Feature Selection for Medical Data Mining: Comparisons of Expert Judgment and Automatic Approaches 医学数据挖掘的特征选择:专家判断与自动方法的比较

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.87

T. Cheng, Chih-Ping Wei, V. Tseng

{"title":"Feature Selection for Medical Data Mining: Comparisons of Expert Judgment and Automatic Approaches","authors":"T. Cheng, Chih-Ping Wei, V. Tseng","doi":"10.1109/CBMS.2006.87","DOIUrl":"https://doi.org/10.1109/CBMS.2006.87","url":null,"abstract":"Data mining refers to the process of automatic extracting previously unknown, valid, and actionable patterns or knowledge from large databases for crucial decision support. Among different data mining technique, classification analysis is widely adopted for healthcare applications for supporting medical diagnostic decisions, improving quality of patient care, etc. If a training dataset contains irrelevant features (i.e., attributes), classification analysis may produce less accurate and less understandable results. Two commonly employed feature selection approaches include use of automatic feature selection mechanisms (i.e., data-driven) or expert judgment (i.e., knowledge-driven). Due to differences in their underlying processes, the two prevailing feature selection approaches may have their unique biases that possibly lead to dissimilar classification effectiveness. In this study, we empirically evaluate the classification effectiveness resulted from the two feature selection approaches on a risk prediction of cardiovascular disease dataset. Our evaluation results suggest that the feature subsets selected domain experts improve the sensitivity of a classifier, while the feature subsets selected by an automatic feature selection mechanism improve the predictive power of a classifier on the majority class (i.e., the specificity in this study)","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126406313","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: 109

Application of Maximum Entropy-Based Image Resizing to Biomedical Imaging 基于最大熵的图像大小调整在生物医学成像中的应用

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.46

P. B. Kao, B. Nutter

引用次数: 1

Audio-Visual Tools for Computer-Assisted Diagnosis of Cardiac Disorders 计算机辅助诊断心脏疾病的视听工具

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.50

Z. Syed, Daniel D. Leeds, Dorothy W. Curtis, J. Guttag

引用次数: 11

Measurement of Relative Brain Atrophy in Neurodegenerative Diseases 神经退行性疾病中相对脑萎缩的测量

19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Pub Date : 2006-06-22 DOI: 10.1109/CBMS.2006.112

D. Abdala, A. V. Wangenheim

引用次数: 0