2014 IEEE 27th International Symposium on Computer-Based Medical Systems最新文献_第8页

A Method for a Real-Time Novel Premature Infant Pain Profile Using High Rate, High Volume Physiological Data Streams 一种使用高速率、高容量生理数据流的实时新型早产儿疼痛分析方法

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.29

Tanvi Naik, A. Thommandram, K. E. S. Fernando, N. Bressan, A. James, C. McGregor

{"title":"A Method for a Real-Time Novel Premature Infant Pain Profile Using High Rate, High Volume Physiological Data Streams","authors":"Tanvi Naik, A. Thommandram, K. E. S. Fernando, N. Bressan, A. James, C. McGregor","doi":"10.1109/CBMS.2014.29","DOIUrl":"https://doi.org/10.1109/CBMS.2014.29","url":null,"abstract":"Management of pain in the neonatal population is one of the most challenging problems in the Neonatal Intensive Care Unit (NICU). There are many gaps in our knowledge of the optimal utilization of the techniques available to assess pain in a valid and reliable manner. Pain that is not treated may cause significant clinical complications in the newborn infant. An initial start was made in a previous study to design a novel Premature Infant Pain Profile with a substantial focus on physiological factors. In this study, the novel Artemis Premature Infant Pain Profile (APIPP) was retrospectively compared with the Premature Infant Pain Profile (PIPP) that is used in the NICU at The Hospital for Sick Children, Toronto, to evaluate the effectiveness and validity of the novel scale being proposed. The main objective of this research was to use a preliminary case study approach by using retrospective data that were assigned PIPP scores to compare the performance of the novel APIPP with the current standard for measuring pain. The experiment was performed with two surgical subjects' data retrospectively with the APIPP algorithm. We demonstrated this study using an environment known as Artemis, a clinical decision support system.","PeriodicalId":398710,"journal":{"name":"2014 IEEE 27th International Symposium on Computer-Based Medical Systems","volume":"734 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122938941","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

Analysis Traceability for Biomedical Researchers 生物医学研究人员的分析可追溯性

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.91

R. McClatchey, Jetendr Shamdasani, A. Branson, K. Munir, Saad Liaquat, G. Frisoni

引用次数: 1

A Novel Model-Based Measure for Quality Evaluation of Image Registration Techniques in DCE-MRI 一种新的基于模型的DCE-MRI图像配准质量评价方法

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.57

S. Marrone, Gabriele Piantadosi, R. Fusco, A. Petrillo, M. Sansone, Carlo Sansone

引用次数: 16

Real-World Data Set Parameters and Synthesization for Matching Identity in Clinical Protocols 临床方案中匹配身份的真实世界数据集参数和综合

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.48

Hanna Farah, Daniel Amyot, K. Emam

引用次数: 2

Dimensionality Reduction with Random Indexing: An Application on Adverse Drug Event Detection Using Electronic Health Records 随机标引降维方法在电子病历药物不良事件检测中的应用

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.22

Isak Karlsson, J. Zhao

{"title":"Dimensionality Reduction with Random Indexing: An Application on Adverse Drug Event Detection Using Electronic Health Records","authors":"Isak Karlsson, J. Zhao","doi":"10.1109/CBMS.2014.22","DOIUrl":"https://doi.org/10.1109/CBMS.2014.22","url":null,"abstract":"Although electronic health records (EHRs) have recently become an important data source for drug safety signals detection, which is usually evaluated in clinical trials, the use of such data is often prohibited by dimensionality and available computer resources. Currently, several methods for reducing dimensionality are developed, used and evaluated within the medical domain. While these methods perform well, the computational cost tends to increase with growing dimensionality. An alternative solution is random indexing, a technique commonly employed in text classification to reduce the dimensionality of large and sparse documents. This study aims to explore how the predictive performance of random forest is affected by dimensionality reduction through random indexing to predict adverse drug reactions (ADEs). Data are extracted from EHRs and the task is to predict whether or not a patient should be assigned an ADE related diagnosis code. Four different dimensionality settings are investigated and their sensitivity, specificity and area under ROC curve are reported for 14 data sets. The results show that for the investigated data sets, the predictive performance is not negatively affected by dimensionality reduction, however, the computational cost is significantly reduced. Therefore, this study concludes that applying random indexing on EHR data reduces the computational cost, while retaining the predictive performance.","PeriodicalId":398710,"journal":{"name":"2014 IEEE 27th International Symposium on Computer-Based Medical Systems","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131808586","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: 4

Liver Segmentation in Contrast Enhanced MR Datasets Using a Probabilistic Active Shape and Appearance Model 基于概率主动形状和外观模型的对比增强MR数据集肝脏分割

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.120

K. Drechsler, Anton Knaub, C. O. Laura, S. Wesarg

引用次数: 4

A Secure Cloud Manager for the X1.V1 Platform X1的安全云管理器。V1平台

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.125

Emanuel Marzini, P. Mori, S. D. Bona, D. Guerri, Marco Lettere, L. Ricci

引用次数: 0

Semantic Search and NLP-Based Diagnostics 语义搜索和基于nlp的诊断

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.68

Y. Kats

引用次数: 0

A Framework for a Social Semantic Registry of IT Skills for Healthcare Workforce 医疗保健工作人员IT技能社会语义注册的框架

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.75

S. Konstantinidis, P. Bamidis

引用次数: 2

FRAT-Up, a Rule-Based System Evaluating Fall Risk in the Elderly 基于规则的老年人跌倒风险评估系统FRAT-Up

2014 IEEE 27th International Symposium on Computer-Based Medical Systems Pub Date : 2014-05-27 DOI: 10.1109/CBMS.2014.35

L. Cattelani, F. Chesani, P. Palumbo, L. Palmerini, S. Bandinelli, C. Becker, L. Chiari

引用次数: 9