2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)最新文献

Automatic analysis of neonatal video data to evaluate resuscitation prformance 新生儿视频数据自动分析评估复苏表现

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Pub Date : 2016-10-01 DOI: 10.1109/ICCABS.2016.7802775

Yue Guo, Johan Wrammert, Kavita Singh, A. Kc, K. Bradford, Ashok K. Krishnamurthy

{"title":"Automatic analysis of neonatal video data to evaluate resuscitation prformance","authors":"Yue Guo, Johan Wrammert, Kavita Singh, A. Kc, K. Bradford, Ashok K. Krishnamurthy","doi":"10.1109/ICCABS.2016.7802775","DOIUrl":"https://doi.org/10.1109/ICCABS.2016.7802775","url":null,"abstract":"Approximately 3% of births require neonatal resuscitation, which has a direct impact on the immediate survival of these infants. This report proposes an automatic video analysis method for neonatal resuscitation performance evaluation, which helps improve the quality of this procedure. More specifically, we design a deep learning based action model which incorporates motion and spatial information in order to classify neonatal resuscitation actions in videos. First, we use a Convolutional Neural Network to select regions containing infants and only keep those that are motion salient. Second, we extract deep spatial-temporal features to train a linear SVM classifier. Finally, we propose a pair-wise model to ensure consistent classification in consecutive frames. We evaluate the proposed method on a dataset consisting of 17 videos and compare the result against the state-of-the-art method for action classification in videos. To our best knowledge, this work is the first to attempt automatic evaluation of neonatal resuscitation videos and identifies several issues that require further work.","PeriodicalId":306466,"journal":{"name":"2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131628862","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

Assessing how multiple mutations affect protein stability using rigid cluster size distributions 使用刚性簇大小分布评估多重突变如何影响蛋白质稳定性

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Pub Date : 2016-10-01 DOI: 10.1109/ICCABS.2016.7802777

E. Andersson, Rebecca Hsieh, Howard Szeto, R. Farhoodi, Nurit Haspel, F. Jagodzinski

{"title":"Assessing how multiple mutations affect protein stability using rigid cluster size distributions","authors":"E. Andersson, Rebecca Hsieh, Howard Szeto, R. Farhoodi, Nurit Haspel, F. Jagodzinski","doi":"10.1109/ICCABS.2016.7802777","DOIUrl":"https://doi.org/10.1109/ICCABS.2016.7802777","url":null,"abstract":"Predicting how amino acid substitutions affect the stability of a protein has relevance to drug design and may help elucidate the mechanisms of disease-causing protein variants. Unfortunately, wet-lab experiments are time intensive, and to the best of our knowledge there are no efficient computational techniques to asses the effect of multiple mutations. In this work we present a new approach for inferring the effects of single and multiple mutations on a protein's structure. Our rMutant algorithm generates in silico mutants with single or multiple amino acid substitutions. We use a graph-theoretic rigidity analysis approach to compute the distributions of rigid cluster sizes of the wild type and mutant structures which we then analyze to infer the effect of the amino acid substitutions. We successfully predict the effects of multiple mutations for which our previous methods were unsuccessful. We validate the predictions of our computational approach against experimental ΔΔG data. To demonstrate the utility of using rigid cluster size distributions to infer the effects of mutations, we also present a Random Forest Machine Learning approach that relies on rigidity data to predict which residues are critical to the stability of a protein. We predict the destabilizing effects of a single or multiple mutations with over 86% accuracy.","PeriodicalId":306466,"journal":{"name":"2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131744072","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

JULiP: An efficient model for accurate intron selection from multiple RNA-seq samples JULiP:从多个RNA-seq样本中精确选择内含子的有效模型

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Pub Date : 2016-10-01 DOI: 10.1109/ICCABS.2016.7802790

Guangyu Yang, L. Florea

{"title":"JULiP: An efficient model for accurate intron selection from multiple RNA-seq samples","authors":"Guangyu Yang, L. Florea","doi":"10.1109/ICCABS.2016.7802790","DOIUrl":"https://doi.org/10.1109/ICCABS.2016.7802790","url":null,"abstract":"Accurate alternative splicing detection and transcript reconstruction are essential to characterize gene regulation and function and to understand development and disease. However, current methods for extracting splicing variation from RNA-seq data only analyze signals from a single sample, which limits transcript reconstruction and fails to detect a complete set of alternative splicing events. We developed a novel feature selection method, JULiP, that analyzes information across multiple samples to identify alternative splicing variation in the form of splice junctions (introns). It formulates the selection problem as a regularized program, utilizing the latent information from multiple RNA-seq samples to construct an accurate and comprehensive intron set. JULiP is highly accurate, and could detect thousands more introns in any one sample, >30% more than the most sensitive single-sample method, and 10% more introns than in the cumulative set of samples, at higher or comparable precision (>98%). Tested assemblers included Cufflinks, CLASS2, StringTie and FlipFlop, and the multi-sample assembler ISP. JULiP is multi-threaded and parallelized, taking only one minute to analyze up to 100 data sets on a multi-computer cluster, and can easily scale up to allow analyses of hundreds and thousands of RNA-seq samples.","PeriodicalId":306466,"journal":{"name":"2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126198990","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

Identifying hotspots in five year survival electronic health records of older adults 识别老年人5年生存电子健康记录中的热点

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Pub Date : 2016-10-01 DOI: 10.1109/ICCABS.2016.7802773

Ankit Agrawal, J. S. Mathias, D. Baker, A. Choudhary

{"title":"Identifying hotspots in five year survival electronic health records of older adults","authors":"Ankit Agrawal, J. S. Mathias, D. Baker, A. Choudhary","doi":"10.1109/ICCABS.2016.7802773","DOIUrl":"https://doi.org/10.1109/ICCABS.2016.7802773","url":null,"abstract":"Understanding the prognosis of older adults is a big challenge in healthcare research, especially since very little is known about how different comorbidities interact and influence the prognosis. Recently, a electronic healthcare records dataset of 24 patient attributes from Northwestern Memorial Hospital was used to develop predictive models for five year survival outcome. In this study we analyze the same data for discovering hotspots with respect to five year survival using association rule mining techniques. The goal here is to identify characteristics of patient segments where the five year survival fraction is significantly lower/higher than the survival fraction across the entire dataset. A two-stage post-processing procedure was used to identify non-redundant rules. The resulting rules conform with existing biomedical knowledge and provide interesting insights into prognosis of older adults. Incorporating such information into clinical decision making could advance person-centered healthcare by encouraging optimal use of healthcare services to those patients most likely to benefit.","PeriodicalId":306466,"journal":{"name":"2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124925662","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: 3

HRVCam: A software for real-time feedback of heart rate and HRV HRVCam:一个实时反馈心率和HRV的软件

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Pub Date : 2016-10-01 DOI: 10.1109/ICCABS.2016.7802767

P. H. Souza, J. O. Ferreira, T. M. D. A. Barbosa, A. Rocha

引用次数: 6

NanoBLASTer: Fast alignment and characterization of Oxford Nanopore single molecule sequencing reads NanoBLASTer:牛津纳米孔单分子测序reads的快速定位和表征

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Pub Date : 2016-10-01 DOI: 10.1109/ICCABS.2016.7802776

M. R. Amin, S. Skiena, M. Schatz

引用次数: 7

An accurate and customizable text classification algorithm: Two applications in healthcare 准确和可定制的文本分类算法:医疗保健中的两个应用

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Pub Date : 2016-10-01 DOI: 10.1109/ICCABS.2016.7802778

Mohammed D. Aldhoayan, Leming Zhou

{"title":"An accurate and customizable text classification algorithm: Two applications in healthcare","authors":"Mohammed D. Aldhoayan, Leming Zhou","doi":"10.1109/ICCABS.2016.7802778","DOIUrl":"https://doi.org/10.1109/ICCABS.2016.7802778","url":null,"abstract":"Text classification is an important step in many data analysis procedures. The demand on text classification algorithm is booming due to the increase of the amount of digital data, especially in the healthcare field. A customizable and accurate algorithm is expected to produce positive impact on the efficiency of many data analysis procedures. In this work, we proposed a novel algorithm for accurately classifying data entries in huge text files into several pre-determined categories. We built the algorithm with multiple rules according to text similarity, frequency, and weight. For different classification tasks, the algorithm can be conveniently adjusted to process the corresponding data sets. Data sets related to healthcare cost analysis (hospital discharge summary) and medical classification systems (ICD-9) are used to evaluate the algorithm. When the algorithm is used on the ICD-9 data, the overall accuracy of the algorithm was 100%. After the algorithm was used on 7480 healthcare cost entries, the results were then compared with the ones processed manually by a physician, and the accuracy was between 86%–91.6%, and the difference is from different classification of ambiguous entries, which is hard to determine the correct category even when it is done manually because those entries were documented improperly. This new classification algorithm is 3 to 5 times faster than the manual process on the same data set. Therefore, this customizable and accurate text classification algorithm is effective in saving time compared to the manual classification methods.","PeriodicalId":306466,"journal":{"name":"2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132498048","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: 3

Curvelet-based texture classification of critical Gleason patterns of prostate histological images 基于曲线的前列腺组织学图像关键Gleason模式纹理分类

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Pub Date : 2016-10-01 DOI: 10.1109/ICCABS.2016.7802768

Wen-Chyi Lin, Ching-Chung Li, J. Epstein, R. Veltri

引用次数: 8

A deep learning-based segmentation method for brain tumor in MR images 基于深度学习的MR图像脑肿瘤分割方法

2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) Pub Date : 2016-10-01 DOI: 10.1109/ICCABS.2016.7802771

Zhe Xiao, Ruohan Huang, Yi Ding, Tian Lan, Rongfen Dong, Zhiguang Qin, Xinjie Zhang, Wei Wang

{"title":"A deep learning-based segmentation method for brain tumor in MR images","authors":"Zhe Xiao, Ruohan Huang, Yi Ding, Tian Lan, Rongfen Dong, Zhiguang Qin, Xinjie Zhang, Wei Wang","doi":"10.1109/ICCABS.2016.7802771","DOIUrl":"https://doi.org/10.1109/ICCABS.2016.7802771","url":null,"abstract":"Accurate tumor segmentation is an essential and crucial step for computer-aided brain tumor diagnosis and surgical planning. Subjective segmentations are widely adopted in clinical diagnosis and treating, but they are neither accurate nor reliable. An automatical and objective system for brain tumor segmentation is strongly expected. But they are still facing some challenges such as lower segmentation accuracy, demanding a priori knowledge or requiring the human intervention. In this paper, a novel and new coarse-to-fine method is proposed to segment the brain tumor. This hierarchical framework consists of preprocessing, deep learning network based classification and post-processing. The preprocessing is used to extract image patches for each MR image and obtains the gray level sequences of image patches as the input of the deep learning network. The deep learning network based classification is implemented by a stacked auto-encoder network to extract the high level abstract feature from the input, and utilizes the extracted feature to classify image patches. After mapping the classification result to a binary image, the post-processing is implemented by a morphological filter to get the final segmentation result. In order to evaluate the proposed method, the experiment was applied to segment the brain tumor for the real patient dataset. The final performance shows that the proposed brain tumor segmentation method is more accurate and efficient.","PeriodicalId":306466,"journal":{"name":"2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125854459","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: 60