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Information Technology in Biomedicine, IEEE Transactions on

Issue 1 • Date Jan. 2011

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Displaying Results 1 - 25 of 28
  • Table of contents

    Publication Year: 2011 , Page(s): C1
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  • IEEE Transactions on Information Technology in Biomedicine publication information

    Publication Year: 2011 , Page(s): C2
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  • Table of contents

    Publication Year: 2011 , Page(s): 1 - 2
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  • Guest Editorial Introduction to the Special Issue on Citizen Centered e-Health Systems in a Global Healthcare Environment: Selected Papers From ITAB 2009

    Publication Year: 2011 , Page(s): 3 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (170 KB) |  | HTML iconHTML  

    The 20 papers in this special issue were originally presented in the International Special Topic Conference on Information Technology in Biomedicine, held in October 2009, in Larnaka, Cyprus. View full abstract»

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  • Noise-Assisted Data Processing With Empirical Mode Decomposition in Biomedical Signals

    Publication Year: 2011 , Page(s): 11 - 18
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (421 KB) |  | HTML iconHTML  

    In this paper, a methodology is described in order to investigate the performance of empirical mode decomposition (EMD) in biomedical signals, and especially in the case of electrocardiogram (ECG). Synthetic ECG signals corrupted with white Gaussian noise are employed and time series of various lengths are processed with EMD in order to extract the intrinsic mode functions (IMFs). A statistical significance test is implemented for the identification of IMFs with high-level noise components and their exclusion from denoising procedures. Simulation campaign results reveal that a decrease of processing time is accomplished with the introduction of preprocessing stage, prior to the application of EMD in biomedical time series. Furthermore, the variation in the number of IMFs according to the type of the preprocessing stage is studied as a function of SNR and time-series length. The application of the methodology in MIT-BIH ECG records is also presented in order to verify the findings in real ECG signals. View full abstract»

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  • Noninvasive Biological Sensor System for Detection of Drunk Driving

    Publication Year: 2011 , Page(s): 19 - 25
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1756 KB) |  | HTML iconHTML  

    Systems capable of monitoring the biological condition of a driver and issuing warnings during instances of drowsiness have recently been studied. Moreover, many researchers have reported that biological signals, such as brain waves, pulsation waves, and heart rate, are different between people who have and have not consumed alcohol. Currently, we are developing a noninvasive system to detect individuals driving under the influence of alcohol by measuring biological signals. We used the frequency time series analysis to attempt to distinguish between normal and intoxicated states of a person as the basis of the sensing system. View full abstract»

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  • Intensive Care Window: Real-Time Monitoring and Analysis in the Intensive Care Environment

    Publication Year: 2011 , Page(s): 26 - 32
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (568 KB) |  | HTML iconHTML  

    This paper introduces a novel, open-source middleware framework for communication with medical devices and an application using the middleware named intensive care window (ICW). The middleware enables communication with intensive care unit bedside-installed medical devices over standard and proprietary communication protocol stacks. The ICW application facilitates the acquisition of vital signs and physiological parameters exported from patient-attached medical devices and sensors. Moreover, ICW provides runtime and post-analysis procedures for data annotation, data visualization, data query, and analysis. The ICW application can be deployed as a stand-alone solution or in conjunction with existing clinical information systems providing a holistic solution to inpatient medical condition monitoring, early diagnosis, and prognosis. View full abstract»

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  • Diagnosis of Cardiovascular Abnormalities From Compressed ECG: A Data Mining-Based Approach

    Publication Year: 2011 , Page(s): 33 - 39
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (566 KB) |  | HTML iconHTML  

    Usage of compressed ECG for fast and efficient telecardiology application is crucial, as ECG signals are enormously large in size. However, conventional ECG diagnosis algorithms require the compressed ECG packets to be decompressed before diagnosis can be performed. This added step of decompression before performing diagnosis for every ECG packet introduces unnecessary delay, which is undesirable for cardiovascular diseased (CVD) patients. In this paper, we are demonstrating an innovative technique that performs real-time classification of CVD. With the help of this real-time classification of CVD, the emergency personnel or the hospital can automatically be notified via SMS/MMS/e-mail when a life-threatening cardiac abnormality of the CVD affected patient is detected. Our proposed system initially uses data mining techniques, such as attribute selection (i.e., selects only a few features from the compressed ECG) and expectation maximization (EM)-based clustering. These data mining techniques running on a hospital server generate a set of constraints for representing each of the abnormalities. Then, the patient's mobile phone receives these set of constraints and employs a rule-based system that can identify each of abnormal beats in real time. Our experimentation results on 50 MIT-BIH ECG entries reveal that the proposed approach can successfully detect cardiac abnormalities (e.g., ventricular flutter/fibrillation, premature ventricular contraction, atrial fibrillation, etc.) with 97% accuracy on average. This innovative data mining technique on compressed ECG packets enables faster identification of cardiac abnormality directly from the compressed ECG, helping to build an efficient telecardiology diagnosis system. View full abstract»

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  • Discrimination Power of Short-Term Heart Rate Variability Measures for CHF Assessment

    Publication Year: 2011 , Page(s): 40 - 46
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (341 KB) |  | HTML iconHTML  

    In this study, we investigated the discrimination power of short-term heart rate variability (HRV) for discriminating normal subjects versus chronic heart failure (CHF) patients. We analyzed 1914.40 h of ECG of 83 patients of which 54 are normal and 29 are suffering from CHF with New York Heart Association (NYHA) classification I, II, and III, extracted by public databases. Following guidelines, we performed time and frequency analysis in order to measure HRV features. To assess the discrimination power of HRV features, we designed a classifier based on the classification and regression tree (CART) method, which is a nonparametric statistical technique, strongly effective on nonnormal medical data mining. The best subset of features for subject classification includes square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD), total power, high-frequencies power, and the ratio between low- and high-frequencies power (LF/HF). The classifier we developed achieved sensitivity and specificity values of 79.3% and 100 %, respectively. Moreover, we demonstrated that it is possible to achieve sensitivity and specificity of 89.7% and 100 %, respectively, by introducing two nonstandard features ΔAVNN and ΔLF/HF, which account, respectively, for variation over the 24 h of the average of consecutive normal intervals (AVNN) and LF/HF. Our results are comparable with other similar studies, but the method we used is particularly valuable because it allows a fully human-understandable description of classification procedures, in terms of intelligible “if ... then ...” rules. View full abstract»

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  • Home-Based Monitoring and Assessment of Parkinson's Disease

    Publication Year: 2011 , Page(s): 47 - 53
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1220 KB) |  | HTML iconHTML  

    As a clinically complex neurodegenerative disease, Parkinson's disease (PD) requires regular assessment and close monitoring. In our current study, we have developed a home-based tool designed to monitor and assess peripheral motor symptoms. An evaluation of the tool was carried out over a period of ten weeks on ten people with idiopathic PD. Participants were asked to use the tool twice daily over four days, once when their medication was working at its best (“on” state) and once when it had worn off (“off” state). Results showed the ability of the data collected to distinguish the “on” and “off” state and also demonstrated statistically significant differences in timed assessments. It is anticipated that this tool could be used in the home environment as an early alert to a change in clinical condition or to monitor the effects of changes in prescribed medications used to manage PD. View full abstract»

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  • Feature Selection and Classification in Supporting Report-Based Self-Management for People with Chronic Pain

    Publication Year: 2011 , Page(s): 54 - 61
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (381 KB) |  | HTML iconHTML  

    Chronic pain is a common long-term condition that affects a person's physical and emotional functioning. Currently, the integrated biopsychosocial approach is the mainstay treatment for people with chronic pain. Self-reporting (the use of questionnaires) is one of the most common methods to evaluate treatment outcome. The questionnaires can consist of more than 300 questions, which is tedious for people to complete at home. This paper presents a machine learning approach to analyze self-reporting data collected from the integrated biopsychosocial treatment, in order to identify an optimal set of features for supporting self-management. In addition, a classification model is proposed to differentiate the treatment stages. Four different feature selection methods were applied to rank the questions. In addition, four supervised learning classifiers were used to investigate the relationships between the numbers of questions and classification performance. There were no significant differences between the feature ranking methods for each classifier in overall classification accuracy or AUC (p >; 0.05); however, there were significant differences between the classifiers for each ranking method (p <; 0.001). The results showed the multilayer perceptron classifier had the best classification performance on an optimized subset of questions, which consisted of ten questions. Its overall classification accuracy and AUC were 100% and 1, respectively. View full abstract»

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  • Developing EMRs in Developing Countries

    Publication Year: 2011 , Page(s): 62 - 65
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (265 KB) |  | HTML iconHTML  

    Clinics in developing nations often use paper-based records that are hard to manage or are inefficient. Electronic medical records (EMR) systems could help increase the efficiency and efficacy of these clinics. Even though some EMR systems have been developed for developing countries, they lack customizability. This paper gives some background information about EMR systems: how they are used in developed countries, and how FileMaker, an off-the-shelf database software, could be used to rapidly deploy EMR systems in clinics and hospitals of developing countries. An existing EMR database developed by Banner Alzheimer's Institute serves as a proof of concept that FileMaker is a viable EMR solution. View full abstract»

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  • MyCare Card Development: Portable GUI Framework for the Personal Electronic Health Record Device

    Publication Year: 2011 , Page(s): 66 - 73
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (350 KB) |  | HTML iconHTML  

    In most emergency situations, health professionals rely on patients to provide information about their medical history. However, in some cases patients might not be able to communicate this information, and in most countries an online integrated patient record system has not been adopted yet. Therefore, in order to address this issue the ongoing project MyCare Card (MyC2, www.myc2.org) has been established. The aim of this project is to design, implement, and evaluate a prototype patient held electronic health record device. Due to the wide range of user requirements, the device, its communication interface, and its software have to be compatible with many common platforms and operating systems. Thus, this paper is addressing one of the software compatibility matters-the cross-platform GUI implementation. It introduces a portable object-oriented GUI framework, suitable for a declarative layout definition, components customization, and fine model-view code separation. It also rationalizes the hardware and software solutions selected for this project implementation. View full abstract»

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  • Using the Dempster–Shafer Theory of Evidence With a Revised Lattice Structure for Activity Recognition

    Publication Year: 2011 , Page(s): 74 - 82
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (570 KB) |  | HTML iconHTML  

    This paper explores a sensor fusion method applied within smart homes used for the purposes of monitoring human activities in addition to managing uncertainty in sensor-based readings. A three-layer lattice structure has been proposed, which can be used to combine the mass functions derived from sensors along with sensor context. The proposed model can be used to infer activities. Following evaluation of the proposed methodology it has been demonstrated that the Dempster-Shafer theory of evidence can incorporate the uncertainty derived from the sensor errors and the sensor context and subsequently infer the activity using the proposed lattice structure. The results from this study show that this method can detect a toileting activity within a smart home environment with an accuracy of 88.2%. View full abstract»

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  • GRISSOM Platform: Enabling Distributed Processing and Management of Biological Data Through Fusion of Grid and Web Technologies

    Publication Year: 2011 , Page(s): 83 - 92
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (669 KB) |  | HTML iconHTML  

    Transcriptomic technologies have a critical impact in the revolutionary changes that reshape biological research. Through the recruitment of novel high-throughput instrumentation and advanced computational methodologies, an unprecedented wealth of quantitative data is produced. Microarray experiments are considered high-throughput, both in terms of data volumes (data intensive) and processing complexity (computationally intensive). In this paper, we present grids for in silico systems biology and medicine (GRISSOM), a web-based application that exploits GRID infrastructures for distributed data processing and management, of DNA microarrays (cDNA, Affymetrix, Illumina) through a generic, consistent, computational analysis framework. GRISSOM performs versatile annotation and integrative analysis tasks, through the use of third-party application programming interfaces, delivered as web services. In parallel, by conforming to service-oriented architectures, it can be encapsulated in other biomedical processing workflows, with the help of workflow enacting software, like Taverna Workbench, thus rendering access to its algorithms, transparent and generic. GRISSOM aims to set a generic paradigm of efficient metamining that promotes translational research in biomedicine, through the fusion of grid and semantic web computing technologies. View full abstract»

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  • Reliable Confidence Measures for Medical Diagnosis With Evolutionary Algorithms

    Publication Year: 2011 , Page(s): 93 - 99
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (367 KB) |  | HTML iconHTML  

    Conformal Predictors (CPs) are machine learning algorithms that can provide predictions complemented with valid confidence measures. In medical diagnosis, such measures are highly desirable, as medical experts can gain additional information for each machine diagnosis. A risk assessment in each prediction can play an important role for medical decision making, in which the outcome can be critical for the patients. Several classical machine learning methods can be incorporated into the CP framework. In this paper, we propose a CP that makes use of evolved rule sets generated by a genetic algorithm (GA). The rule-based GA has the advantage of being human readable. We apply our method on two real-world datasets for medical diagnosis, one dataset for breast cancer diagnosis, which contains data gathered from fine needle aspirate of breast mass; and one dataset for ovarian cancer diagnosis, which contains proteomic patterns identified in serum. Our results on both datasets show that the proposed method is as accurate as the classical techniques, while it provides reliable and useful confidence measures. View full abstract»

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  • Intuitionistic Fuzzy Cognitive Maps for Medical Decision Making

    Publication Year: 2011 , Page(s): 100 - 107
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (550 KB) |  | HTML iconHTML  

    Medical decision making can be regarded as a process, combining both analytical cognition and intuition. It involves reasoning within complex causal models of multiple concepts, usually described by uncertain, imprecise, and/or incomplete information. Aiming to model medical decision making, we propose a novel approach based on cognitive maps and intuitionistic fuzzy logic. The new model, called intuitionistic fuzzy cognitive map (iFCM), extends the existing fuzzy cognitive map (FCM) by considering the expert's hesitancy in the determination of the causal relations between the concepts of a domain. Furthermore, a modification in the formulation of the new model makes it even less sensitive than the original model to missing input data. To validate its effectiveness, an iFCM with 34 concepts representing fuzzy, linguistically expressed patient-specific data, symptoms, and multimodal measurements was constructed for pneumonia severity assessment. The results obtained reveal its comparative advantage over the respective FCM model by providing decisions that match better with the ones made by the experts. The generality of the proposed approach suggests its suitability for a variety of medical decision-making tasks. View full abstract»

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  • A Holistic Environment for the Design and Execution of Self-Adaptive Clinical Pathways

    Publication Year: 2011 , Page(s): 108 - 118
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1025 KB) |  | HTML iconHTML  

    One of the main challenges to be confronted by modern health care, so as to increase treatment quality, is the personalization of treatment. The treatment personalization requires the continuous reconfiguration and adaptation of the selected treatment schemes according to the “current” clinical status of each patient and “current” circumstances inside a health care organization that change rapidly, as well as the updated medical knowledge. In this paper, we present an innovative software environment that provides an integrated IT solution concerning the adaptation of health care processes (clinical pathways) during execution time. The software comprises a health care process execution engine assisted by a semantic infrastructure for reconfiguring the clinical pathways. During the execution of clinical pathways, the system reasons over the rules and reconfigures the next steps of the treatment. A graphical designer interface is implemented for the definition of the rule-set for the clinical pathways adaptation in a user-friendly way. View full abstract»

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  • Multiscale Amplitude-Modulation Frequency-Modulation (AM–FM) Texture Analysis of Multiple Sclerosis in Brain MRI Images

    Publication Year: 2011 , Page(s): 119 - 129
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (709 KB) |  | HTML iconHTML  

    This study introduces the use of multiscale amplitude modulation-frequency modulation (AM-FM) texture analysis of multiple sclerosis (MS) using magnetic resonance (MR) images from brain. Clinically, there is interest in identifying potential associations between lesion texture and disease progression, and in relating texture features with relevant clinical indexes, such as the expanded disability status scale (EDSS). This longitudinal study explores the application of 2-D AM-FM analysis of brain white matter MS lesions to quantify and monitor disease load. To this end, MS lesions and normal-appearing white matter (NAWM) from MS patients, as well as normal white matter (NWM) from healthy volunteers, were segmented on transverse T2-weighted images obtained from serial brain MR imaging (MRI) scans (0 and 6-12 months). The instantaneous amplitude (IA), the magnitude of the instantaneous frequency (IF), and the IF angle were extracted from each segmented region at different scales. The findings suggest that AM-FM characteristics succeed in differentiating 1) between NWM and lesions; 2) between NAWM and lesions; and 3) between NWM and NAWM. A support vector machine (SVM) classifier succeeded in differentiating between patients that, two years after the initial MRI scan, acquired an EDSS ≤ 2 from those with EDSS >; 2 (correct classification rate = 86%). The best classification results were obtained from including the combination of the low-scale IA and IF magnitude with the medium-scale IA. The AM-FM features provide complementary information to classical texture analysis features like the gray-scale median, contrast, and coarseness. The findings of this study provide evidence that AM-FM features may have a potential role as surrogate markers of lesion load in MS. View full abstract»

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  • Comparison of Multiresolution Features for Texture Classification of Carotid Atherosclerosis From B-Mode Ultrasound

    Publication Year: 2011 , Page(s): 130 - 137
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (559 KB) |  | HTML iconHTML  

    In this paper, a multiresolution approach is suggested for texture classification of atherosclerotic tissue from B-mode ultrasound. Four decomposition schemes, namely, the discrete wavelet transform, the stationary wavelet transform, wavelet packets (WP), and Gabor transform (GT), as well as several basis functions, were investigated in terms of their ability to discriminate between symptomatic and asymptomatic cases. The mean and standard deviation of the detail subimages produced for each decomposition scheme were used as texture features. Feature selection included 1) ranking the features in terms of their divergence values and 2) appropriately thresholding by a nonlinear correlation coefficient. The selected features were subsequently input into two classifiers using support vector machines (SVM) and probabilistic neural networks. WP analysis and the coiflet 1 produced the highest overall classification performance (90% for diastole and 75% for systole) using SVM. This might reflect WP's ability to reveal differences in different frequency bands, and therefore, characterize efficiently the atheromatous tissue. An interesting finding was that the dominant texture features exhibited horizontal directionality, suggesting that texture analysis may be affected by biomechanical factors (plaque strains). View full abstract»

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  • Phase-Based Level Set Segmentation of Ultrasound Images

    Publication Year: 2011 , Page(s): 138 - 147
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (886 KB) |  | HTML iconHTML  

    Ultrasonic image segmentation is a difficult problem due to speckle noise, low contrast, and local changes of intensity. Intensity-based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity invariant. Here, we use level set propagation to capture the left ventricle boundaries. The proposed approach uses a new speed term based on local phase and local orientation derived from the monogenic signal, which makes the algorithm robust to attenuation artifact. Furthermore, we use Cauchy kernels, as a better alternative to the commonly used log-Gabor, as pair of quadrature filters for the feature extraction. Results on synthetic and natural data show that the proposed method can robustly handle noise, and captures well the low contrast boundaries. View full abstract»

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  • Effect of Posture Change on the Geometric Features of the Healthy Carotid Bifurcation

    Publication Year: 2011 , Page(s): 148 - 154
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (551 KB) |  | HTML iconHTML  

    Segmented cross-sectional MRI images were used to construct 3-D virtual models of the carotid bifurcation in ten healthy volunteers. Geometric features, such as bifurcation angle, internal carotid artery (ICA) angle, planarity angle, asymmetry angle, tortuosity, curvature, bifurcation area ratio, ICA/common carotid artery (CCA), external carotid artery (ECA)/CCA, and ECA/ICA diameter ratios, were calculated for both carotids in two head postures: 1) the supine neutral position; and 2) the prone sleeping position with head rotation to the right (~80°). The results obtained have shown that head rotation causes 1) significant variations in bifurcation angle [32% mean increase for the right carotid (RC) and 21% mean decrease for the left carotid (LC)] and internal carotid artery angle (97% mean increase for the RC, 43% mean decrease for the LC); 2) a slight increase in planarity and asymmetry angles for both RC and LC; 3) minor and variable curvature changes for the CCA and for the branches; 4) slight tortuosity changes for the braches but not for the CCA; and 5) unsubstantial alterations in area and diameter ratios (percentage changes <;10%). The significant geometric changes observed in most subjects with head posture may also cause significant changes in bifurcation hemodynamics and warrant future investigation of the hemodynamic parameters related to the development of atherosclerotic disease such as low oscillating wall shear stress and particle residence times. View full abstract»

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  • Complementary Gene Signature Integration in Multiplatform Microarray Experiments

    Publication Year: 2011 , Page(s): 155 - 163
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (678 KB) |  | HTML iconHTML  

    The concept of gene signature overlap has been addressed previously in a number of research papers. A common conclusion is the absence of significant overlap. In this paper, we verify the aforementioned fact, but we also assess the issue of similarities not on the gene level, but on the biology level hidden underneath a given signature. We proceed by taking into account the biological knowledge that exists among different signatures, and use it as a means of integrating them and refining their statistical significance on the datasets. In this form, by integrating biological knowledge with information stemming from data distributions, we derive a unified signature that is significantly improved over its predecessors in terms of performance and robustness. Our motive behind this approach is to assess the problem of evaluating different signatures not in a competitive but rather in a complementary manner, where one is treated as a pool of knowledge contributing to a global and unified solution. View full abstract»

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  • Depicting Educational Content Repurposing Context and Inheritance

    Publication Year: 2011 , Page(s): 164 - 170
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (319 KB) |  | HTML iconHTML  

    Educational content is often shared among different educators and is enriched, adapted, and, in general, repurposed so that it can be reused in different contexts. This paper discusses educational content and content repurposing in medical education, presenting different repurposing contexts. Finally, it proposes a novel approach to content repurposing via Web 2.0 social networking of learning resources. The proposed social network is augmented by a graphical representation module in order to capture and depict the relationships among different repurposed medical educational resources, based on educational resource “families” and inheritance. The ultimate goal is to provide a conceptually different approach to educational resource organization and retrieval via “social” associations among learning resources. View full abstract»

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    Publication Year: 2011 , Page(s): 171
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    Freely Available from IEEE

Aims & Scope

The IEEE Transactions on Information Technology in Biomedicine publishes basic and applied papers of information technology applications in health, healthcare and biomedicine.

 

This Transaction ceased publication in 2012. The current retitled publication is IEEE Journal of Biomedical and Health Informatics.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Yuan-ting Zhang
427, Ho Sin Hang Engineering Building, The Chinese
University of Hong Kong, Shatin, NT, Hong Kong
ytzhang@ee.cuhk.edu.hk
Phone:+852 2609-8458
Fax:+852 2609-5558