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Information Technology in Biomedicine, IEEE Transactions on

Issue 2 • Date March 2011

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  • Table of contents

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  • IEEE Transactions on Information Technology in Biomedicine publication information

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  • Table of contents

    Page(s): 173 - 174
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  • Editorial - note on biomedical and health informatics

    Page(s): 175 - 177
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  • Multiscale Amplitude-Modulation Frequency-Modulation (AM–FM) Texture Analysis of Ultrasound Images of the Intima and Media Layers of the Carotid Artery

    Page(s): 178 - 188
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (638 KB) |  | HTML iconHTML  

    The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Clinically, there is strong interest in identifying how the composition and texture of the media layer (ML) can be associated with the risk of stroke. In this study, we use 2-D amplitude-modulation frequency-modulation (AM-FM) analysis of the intima-media complex (IMC), the ML, and intima layer (IL) of the CCA to detect texture changes as a function of age and sex. The study was performed on 100 ultrasound images acquired from asymptomatic subjects at risk of atherosclerosis. To investigate texture variations associated with age, we separated them into three age groups: 1) patients younger than 50; 2) patients aged between 50 and 60 years old; and 3) patients over 60 years old. We also separated the patients by sex. The IMC, ML, and IL were segmented manually by a neurovascular expert and also by a snake-based segmentation system. To reject strong edge artifacts, we prefilter with an AM-FM filterbank that is centered along the horizontal frequency axis (parallel to the long axis of the IMC, ML, and IL), while removing the low-pass filter estimates and frequency bands with large, vertical frequency components. To investigate significant texture changes, we extract the instantaneous amplitude (IA) and the magnitude of the instantaneous frequency (IF) over each layer component, for low-, medium-, and high-frequency AM-FM components. We detected significant texture differences between the higher risk age group of >;60 years versus the lower risk age group of <;50 and the 50-60 group. In particular, between the <;50 and >;60 groups, we found significant differences in the medium-scale IA extracted from the IMC. Between the >;60 and the 50-60 groups, we found significant texture changes in the low scale IA and high-scale IF magnitude extracted from the IMC, and the low-scale IA extracted from the IL. Also, we noted t- - hat the IA for the ML showed significant differences between males and females for all age groups. The AM-FM features provide complimentary information to classical texture analysis features like the gray-scale median, contrast, and coarseness. These findings provide evidence that AM-FM texture features can be associated with the progression of cardiovascular risk for disease and the risk of stroke with age. However, a larger scale study is needed to establish the application in clinical practice. View full abstract»

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  • Hemodynamic Flow Modeling Through an Abdominal Aorta Aneurysm Using Data Mining Tools

    Page(s): 189 - 194
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (665 KB) |  | HTML iconHTML  

    Geometrical changes of blood vessels, called aneurysm, occur often in humans with possible catastrophic outcome. Then, the blood flow is enormously affected, as well as the blood hemodynamic interaction forces acting on the arterial wall. These forces are the cause of the wall rupture. A mechanical quantity characteristic for the blood-wall interaction is the wall shear stress, which also has direct physiological effects on the endothelial cell behavior. Therefore, it is very important to have an insight into the blood flow and shear stress distribution when an aneurysm is developed in order to help correlating the mechanical conditions with the pathogenesis of pathological changes on the blood vessels. This insight can further help in improving the prevention of cardiovascular diseases evolution. Computational fluid dynamics (CFD) has been used in general as a tool to generate results for the mechanical conditions within blood vessels with and without aneurysms. However, aneurysms are very patient specific and reliable results from CFD analyses can be obtained by a cumbersome and time-consuming process of the computational model generation followed by huge computations. In order to make the CFD analyses efficient and suitable for future everyday clinical practice, we have here employed data mining (DM) techniques. The focus was to combine the CFD and DM methods for the estimation of the wall shear stresses in an abdominal aorta aneurysm (AAA) underprescribed geometrical changes. Additionally, computing on the grid infrastructure was performed to improve efficiency, since thousands of CFD runs were needed for creating machine learning data. We used several DM techniques and found that our DM models provide good prediction of the shear stress at the AAA in comparison with full CFD model results on real patient data. View full abstract»

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  • A Hybrid Clustering Method for ROI Delineation in Small-Animal Dynamic PET Images: Application to the Automatic Estimation of FDG Input Functions

    Page(s): 195 - 205
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (720 KB) |  | HTML iconHTML  

    Tracer kinetic modeling with dynamic positron emission tomography (PET) requires a plasma time-activity curve (PTAC) as an input function. Several image-derived input function (IDIF) methods that rely on drawing the region of interest (ROI) in large vascular structures have been proposed to overcome the problems caused by the invasive approach for obtaining the PTAC, especially for small-animal studies. However, the manual placement of ROIs for estimating IDIF is subjective and labor-intensive, making it an undesirable and unreliable process. In this paper, we propose a novel hybrid clustering method (HCM) that objectively delineates ROIs in dynamic PET images for the estimation of IDIFs, and demonstrate its application to the mouse PET studies acquired with [ 18F]Fluoro-2-deoxy-2-D-glucose (FDG). We begin our HCM using k-means clustering for background removal. We then model the time-activity curves using polynomial regression mixture models in curve clustering for heart structure detection. The hierarchical clustering is finally applied for ROI refinements. The HCM achieved accurate ROI delineation in both computer simulations and experimental mouse studies. In the mouse studies, the predicted IDIF had a high correlation with the gold standard, the PTAC derived from the invasive blood samples. The results indicate that the proposed HCM has a great potential in ROI delineation for automatic estimation of IDIF in dynamic FDG-PET studies. View full abstract»

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  • Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI

    Page(s): 206 - 213
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (750 KB) |  | HTML iconHTML  

    Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients. View full abstract»

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  • Vessel Tree Segmentation in Presence of Interstitial Lung Disease in MDCT

    Page(s): 214 - 220
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (643 KB) |  | HTML iconHTML  

    The automated segmentation of vessel tree structures is a crucial preprocessing stage in computer aided diagnosis (CAD) schemes of interstitial lung disease (ILD) patterns in multidetector computed tomography (MDCT). The accuracy of such preprocessing stages is expected to influence the accuracy of lung CAD schemes. Although algorithms aimed at improving the accuracy of lung fields segmentation in presence of ILD have been reported, the corresponding vessel tree segmentation stage is under-researched. Furthermore, previously reported vessel tree segmentation methods have only dealt with normal lung parenchyma. In this paper, an automated vessel tree segmentation scheme is proposed, adapted to the presence of pathologies affecting lung parenchyma. The first stage of the method accounts for a recently proposed method utilizing a 3-D multiscale vessel enhancement filter based on eigenvalue analysis of the Hessian matrix and on unsupervised segmentation. The second stage of the method is a texture-based voxel classification refinement to correct possible over-segmentation. The performance of the proposed scheme, and of the previously reported technique, in vessel tree segmentation was evaluated by means of area overlap (previously reported: 0.715 ± 0.082, proposed: 0.931 ± 0.027), true positive fraction (previously reported: 0.968 ± 0.019, proposed: 0.935 ± 0.036) and false positive fraction (previously reported: 0.400 ± 0.181, proposed: 0.074 ± 0.031) on a dataset of 210 axial slices originating from seven ILD affected patient scans (used for performance evaluation out of 15). The pro posed method demonstrated a statistically significantly (p <; 0.05) higher performance as compared to the previously reported vessel tree segmentation technique. The impact of suboptimal vessel tree segmentation in a reticular pattern quantification system is also demonstrated. View full abstract»

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  • Salient Feature Region: A New Method for Retinal Image Registration

    Page(s): 221 - 232
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (967 KB) |  | HTML iconHTML  

    Retinal image registration is crucial for the diagnoses and treatments of various eye diseases. A great number of methods have been developed to solve this problem; however, fast and accurate registration of low-quality retinal images is still a challenging problem since the low content contrast, large intensity variance as well as deterioration of unhealthy retina caused by various pathologies. This paper provides a new retinal image registration method based on salient feature region (SFR). We first propose a well-defined region saliency measure that consists of both local adaptive variance and gradient field entropy to extract the SFRs in each image. Next, an innovative local feature descriptor that combines gradient field distribution with corresponding geometric information is then computed to match the SFRs accurately. After that, normalized cross-correlation-based local rigid registration is performed on those matched SFRs to refine the accuracy of local alignment. Finally, the two images are registered by adopting high-order global transformation model with locally well-aligned region centers as control points. Experimental results show that our method is quite effective for retinal image registration. View full abstract»

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  • Automated Detection of Cell Nuclei in Pap Smear Images Using Morphological Reconstruction and Clustering

    Page(s): 233 - 241
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (996 KB) |  | HTML iconHTML  

    In this paper, we present a fully automated method for cell nuclei detection in Pap smear images. The locations of the candidate nuclei centroids in the image are detected with morphological analysis and they are refined in a second step, which incorporates a priori knowledge about the circumference of each nucleus. The elimination of the undesirable artifacts is achieved in two steps: the application of a distance-dependent rule on the resulted centroids; and the application of classification algorithms. In our method, we have examined the performance of an unsupervised (fuzzy C-means) and a supervised (support vector machines) classification technique. In both classification techniques, the effect of the refinement step improves the performance of the clustering algorithm. The proposed method was evaluated using 38 cytological images of conventional Pap smears containing 5617 recognized squamous epithelial cells. The results are very promising, even in the case of images with high degree of cell overlapping. View full abstract»

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  • Automated Detection of White Matter Changes in Elderly People Using Fuzzy, Geostatistical, and Information Combining Models

    Page(s): 242 - 250
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1343 KB) |  | HTML iconHTML  

    Detection of white matter changes of the brain using magnetic resonance imaging (MRI) has increasingly been an active and challenging research area in computational neuroscience. There have rarely been any single image analysis methods that can effectively address the issue of automated quantification of neuroimages, which are subject to different interests of various medical hypotheses. This paper presents new image segmentation models for automated detection of white matter changes of the brain in an elderly population. The methods are based on the computational models of fuzzy clustering, possibilistic clustering, geostatistics, and knowledge combination. Experimental results on MRI data have shown that the proposed image analysis methodology can be applied as a very useful computerized tool for the validation of our particular medical question, where white matter changes of the brain are thought to be the most important social medical evidence. View full abstract»

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  • Predicting Breast Screening Attendance Using Machine Learning Techniques

    Page(s): 251 - 259
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (708 KB) |  | HTML iconHTML  

    Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale. View full abstract»

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  • A Medical-Grade Wireless Architecture for Remote Electrocardiography

    Page(s): 260 - 267
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (776 KB) |  | HTML iconHTML  

    In telecardiology, electrocardiogram (ECG) signals from a patient are acquired by sensors and transmitted in real time to medical personnel across a wireless network. The use of IEEE 802.11 wireless LANs (WLANs), which are already deployed in many hospitals, can provide ubiquitous connectivity and thus allow cardiology patients greater mobility. However, engineering issues, including the error-prone nature of wireless channels and the unpredictable delay and jitter due to the nondeterministic nature of access to the wireless medium, need to be addressed before telecardiology can be safely realized. We propose a medical-grade WLAN architecture for remote ECG monitoring, which employs the point-coordination function (PCF) for medium access control and Reed-Solomon coding for error control. Realistic simulations with uncompressed two-lead ECG data from the MIT-BIH arrhythmia database demonstrate reliable wireless ECG monitoring; the reliability of ECG transmission exceeds 99.99% with the initial buffering delay of only 2.4 s. View full abstract»

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  • Distributed Intelligent Sensor Network for the Rehabilitation of Parkinson's Patients

    Page(s): 268 - 276
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (865 KB) |  | HTML iconHTML  

    The coordination between locomotion and respiration of Parkinson's disease (PD) patients is reduced or even absent. The degree of this disturbance is assumed to be associated with the disease severity [S. Schiermeier, D. Schäfer, T. Schäfer, W. Greulich, and M. E. Schläfke, “Breathing and locomotion in patients with Parkinson's disease,” Eur. J. Physiol., vol. 443, No. 1, pp. 67-71, Jul. 2001]. To enable a long-term and online analysis of the locomotion-respiration coordination for scientific purpose, we have developed a distributed wireless communicating network. We aim to integrate biofeedback protocols with the real-time analysis of the locomotion-respiration coordination in the system to aid rehabilitation of PD patients. The network of sensor nodes is composed of intelligent network operating devices (iNODEs). The miniaturized iNODE contains a continuous data acquisition system based on microcontroller, local data storage, capability of on-sensor digital signal processing in real time, and wireless communication based on IEEE 802.15.4. Force sensing resistors and respiratory inductive plethysmography are applied for motion and respiration sensing, respectively. A number of experiments have been undertaken in clinic and laboratory to test the system. It shall facilitate identification of therapeutic effects on PD, allowing to measure the patients' health status, and to aid in the rehabilitation of PD patients. View full abstract»

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  • Emergency Fall Incidents Detection in Assisted Living Environments Utilizing Motion, Sound, and Visual Perceptual Components

    Page(s): 277 - 289
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    This paper presents the implementation details of a patient status awareness enabling human activity interpretation and emergency detection in cases, where the personal health is threatened like elder falls or patient collapses. The proposed system utilizes video, audio, and motion data captured from the patient's body using appropriate body sensors and the surrounding environment, using overhead cameras and microphone arrays. Appropriate tracking techniques are applied to the visual perceptual component enabling the trajectory tracking of persons, while proper audio data processing and sound directionality analysis in conjunction to motion information and subject's visual location can verify fall and indicate an emergency event. The postfall visual and motion behavior of the subject, which indicates the severity of the fall (e.g., if the person remains unconscious or patient recovers) is performed through a semantic representation of the patient's status, context and rules-based evaluation, and advanced classification. A number of advanced classification techniques have been examined in the framework of this study and their corresponding performance in terms of accuracy and efficiency in detecting an emergency situation has been thoroughly assessed. View full abstract»

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  • Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution

    Page(s): 290 - 300
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1253 KB) |  | HTML iconHTML  

    According to the demographic evolution in industrialized countries, more and more elderly people will experience falls at home and will require emergency services. The main problem comes from fall-prone elderly living alone at home. To resolve this lack of safety, we propose a new method to detect falls at home, based on a multiple-cameras network for reconstructing the 3-D shape of people. Fall events are detected by analyzing the volume distribution along the vertical axis, and an alarm is triggered when the major part of this distribution is abnormally near the floor during a predefined period of time, which implies that a person has fallen on the floor. This method was validated with videos of a healthy subject who performed 24 realistic scenarios showing 22 fall events and 24 cofounding events (11 crouching position, 9 sitting position, and 4 lying on a sofa position) under several camera configurations, and achieved 99.7% sensitivity and specificity or better with four cameras or more. A real-time implementation using a graphic processing unit (GPU) reached 10 frames per second (fps) with 8 cameras, and 16 fps with 3 cameras. View full abstract»

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  • Addressing Mental Health Epidemic Among University Students via Web-based, Self-Screening, and Referral System: A Preliminary Study

    Page(s): 301 - 307
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (570 KB) |  | HTML iconHTML  

    The prevalence and severity of mental health problems in college and university communities are alarming. However, the majority of students with mental disorders do not seek help from professionals. To help students assess their mental conditions and encourage them to take an active role in seeking care, we developed a web-based self-screening, referral, and secure communication system and evaluated it at the University of Washington for 17 months. The system handled more than 1000 screenings during the study period. Of the subjects who used the system, 75% noted that the system helped them to make a decision to receive help from professionals. The system was able to provide outreach to students with mental health concerns effectively, allow them to self-screen their conditions, and encourage them to receive professional assistance. The system provided students with 24/7 web-based access to the clinic, and more than 50% of the system use was made during off-hours. The system was well received by patients, referral managers, and care providers, and it was transferred to the clinic for daily clinical use. We believe that a web-based system like ours could be used as one way to tackle the growing epidemic of mental health problems among college and university students. View full abstract»

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  • Genetic-Algorithm-Based Multiple Regression With Fuzzy Inference System for Detection of Nocturnal Hypoglycemic Episodes

    Page(s): 308 - 315
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    Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures, and even death. It is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemic monitor is a noninvasive monitor that measures some physiological parameters continuously to provide detection of hypoglycemic episodes in type 1 diabetes mellitus patients (T1DM). Based on heart rate (HR), corrected QT interval of the ECG signal, change of HR, and the change of corrected QT interval, we develop a genetic algorithm (GA)-based multiple regression with fuzzy inference system (FIS) to classify the presence of hypoglycemic episodes. GA is used to find the optimal fuzzy rules and membership functions of FIS and the model parameters of regression method. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes is associated with HRs and corrected QT intervals. The overall data were organized into a training set (eight patients) and a testing set (another eight patients) randomly selected. The results show that the proposed algorithm performs a good sensitivity with an acceptable specificity. View full abstract»

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  • Privilege Management Infrastructure for Virtual Organizations in Healthcare Grids

    Page(s): 316 - 323
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (847 KB) |  | HTML iconHTML  

    This paper is focused on the management of virtual organizations (VO) inside healthcare environments where grid technology is used as middleware for a healthcare services-oriented architecture (HSOA). Some of the main tasks considered for the provision of an efficient VO management are management of users, assignation of roles to users, assignation of privileges to roles, and definition of resources access policies. These tasks are extremely close to privilege management infrastructures (PMI), so we face VO management services as part of the PMI supporting access control to healthcare resources inside the HSOA. In order to achieve a completely open and interoperable PMI, we review and apply standards of security and architectural design. Moreover, semantic technologies are introduced in decision points for access control allowing the management of a high degree of descriptors by means of ontologies and infer the decision making through rules and reasoners. View full abstract»

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  • An Ontology-Based System for Context-Aware and Configurable Services to Support Home-Based Continuous Care

    Page(s): 324 - 333
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    Continuous care models for chronic diseases pose several technology-oriented challenges for home-based care, where assistance services rely on a close collaboration among different stakeholders, such as health operators, patient relatives, and social community members. This paper describes an ontology-based context model and a related context management system providing a configurable and extensible service-oriented framework to ease the development of applications for monitoring and handling patient chronic conditions. The system has been developed in a prototypal version, and integrated with a service platform for supporting operators of home-based care networks in cooperating and sharing patient-related information and coordinating mutual interventions for handling critical and alarm situations. Finally, we discuss experimentation results and possible further research directions. View full abstract»

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  • Dynamic Composition of Semantic Pathways for Medical Computational Problem Solving by Means of Semantic Rules

    Page(s): 334 - 343
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (885 KB) |  | HTML iconHTML  

    This paper presents a semantic rule-based system for the composition of successful algorithmic pathways capable of solving medical computational problems (MCPs). A subset of medical algorithms referring to MCP solving concerns well-known medical problems and their computational algorithmic solutions. These solutions result from computations within mathematical models aiming to enhance healthcare quality via support for diagnosis and treatment automation, especially useful for educational purposes. Currently, there is a plethora of computational algorithms on the web, which pertain to MCPs and provide all computational facilities required to solve a medical problem. An inherent requirement for the successful construction of algorithmic pathways for managing real medical cases is the composition of a sequence of computational algorithms. The aim of this paper is to approach the composition of such pathways via the design of appropriate finite-state machines (FSMs), the use of ontologies, and SWRL semantic rules. The goal of semantic rules is to automatically associate different algorithms that are represented as different states of the FSM in order to result in a successful pathway. The rule-based approach is herein implemented on top of Knowledge-Based System for Intelligent Computational Search in Medicine (KnowBaSICS-M), an ontology-based system for MCP semantic management. Preliminary results have shown that the proposed system adequately produces algorithmic pathways in agreement with current international medical guidelines. View full abstract»

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  • Factors Affecting Acceptance of a Web-Based Self-Referral System

    Page(s): 344 - 347
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    With the growing availability of health information on the web, people are becoming more knowledgeable on their health conditions and treatment options, and more patients seek specialists by themselves. To aid patients in requesting self-referrals, we have developed and evaluated a web-based self-referral system in three specialty clinics at the University of Washington. Two clinics adopted the system for routine clinical use, while the third clinic decided not to. A major difference between these two groups was in how fast online requests from patients were handled, which significantly influenced patients' satisfaction. Clinic's preparedness for handling the temporarily increased workload due to the introduction of a new health information system played a role as well. Also, we noticed that the physician leadership/championship made a difference in the acceptance of our system. View full abstract»

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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.

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Editor-in-Chief
Yuan-ting Zhang
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