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Information Technology in Biomedicine, IEEE Transactions on

Issue 1 • Date Jan. 2006

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Displaying Results 1 - 25 of 28
  • Table of contents

    Page(s): c1 - c4
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  • IEEE Transactions on Information Technology in Biomedicine publication information

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  • Editorial: TITB Enters Its Tenth Year; Its Founding Editor-in-Chief Passed Away

    Page(s): 1
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  • In Memoriam—Swamy Laxminarayan (1939-2005)

    Page(s): 2 - 4
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  • A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection

    Page(s): 5 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (166 KB) |  | HTML iconHTML  

    White blood cell detection is one of the most basic and key steps in the automatic recognition system of white blood cells in microscopic blood images. Its accuracy and stability greatly affect the operating speed and recognition accuracy of the whole system. But there are only a few methods available for cell detection or segmentation due to the complexity of the microscopic images. This paper focuses on this issue. Based on the detailed analysis of the existing two methods-threshold segmentation followed by mathematical morphology (TSMM), and the fuzzy logic method-a new detection algorithm (NDA) based on fuzzy cellular neural networks is proposed. NDA combines the advantages of TSMM and the fuzzy logic method, and overcomes their drawbacks. With NDA, we can detect almost all white blood cells, and the contour of each detected cell is nearly complete. Its adaptability is strong and the running speed is expected to be comparatively high due to the easy hardware implementation of FCN. Experimental results show good performance View full abstract»

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  • Combining rate-adaptive cardiac pacing algorithms via multiagent negotiation

    Page(s): 11 - 18
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    Simulating and controlling physiological phenomena are notoriously complex tasks to tackle and require accurate models of the phenomena of interest. Currently, most physiological processes are described by a set of partial models capturing specific aspects of the phenomena, and usually their composition does not produce effective comprehensive models. A current open issue is thus the development of techniques able to effectively describe a phenomenon starting from partial models. This is particularly relevant for heart rate regulation modeling where a large number of heterogeneous partial models exists. In this paper we make the original proposal of adopting a multiagent paradigm, called anthropic agency, to provide a powerful and flexible tool for combining partial models of heart rate regulation for adaptive cardiac pacing applications. The partial models are embedded in autonomous computational entities, called agents, that cooperatively negotiate in order to smooth their conflicts on the values of the variables forming the global model the multiagent system provides. We experimentally evaluate our approach and we analyze its properties View full abstract»

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  • TMABoost: an integrated system for comprehensive management of tissue microarray data

    Page(s): 19 - 27
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    In the last decade, high-throughput technologies such as DNA and tissue microarrays (TMAs) have become a means of large-scale investigation of gene expression, providing a plethora of new biomedical data in a relatively short time. Data collection and organization are critical aspects in this process to ensure the quality and reliability of future data interpretation. In this work, we propose a comprehensive approach to handle TMA data with the aim of supporting and promoting biomarker development. We describe a web-based system for the complete management of tissue microarray data in the field of pathology. The system has been in use since June, 2003. Our approach includes automatic localization and identification of tissue microarray samples, and quantitative image analysis that allows high-throughput screening of TMAs by ensuring nonsubjective measures and novel prognosis associations. In this paper, we present the architecture and the components of this system View full abstract»

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  • Intelligent inferencing and haptic simulation for Chinese acupuncture learning and training

    Page(s): 28 - 41
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    This paper presents an intelligent virtual environment for Chinese acupuncture learning and training using state-of-the-art virtual reality technology. It is the first step toward developing a comprehensive virtual human model for studying Chinese medicine. Students can learn and practice acupuncture in the proposed 3-D interactive virtual environment that supports a force feedback interface for needle insertion. Thus, students not only "see" but also "touch" the virtual patient. With high performance computers, highly informative and flexible visualization of acupuncture points of various related meridian and collateral can be highlighted to guide the students during training. A computer-based expert system using our newly proposed intelligent fuzzy petri net is designed and implemented to train the students to treat different diseases using acupuncture. Such an intelligent virtual reality system can provide an interesting and effective learning environment for Chinese acupuncture View full abstract»

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  • Visual positioning of previously defined ROIs on microscopic slides

    Page(s): 42 - 50
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    In microscopy, regions of interest are usually much smaller than the whole slide area. Various microscopy related medical applications, such as telepathology and computer aided diagnosis, are liable to benefit greatly from microscope auto positioning on previously defined regions of interest. In this paper, we present a method for image-based auto positioning on a microscope slide. The method is based on localization of a microscopic query image using a previously acquired slide map. It uses geometric hashing, a highly efficient technique drawn from the object recognition field. The algorithm exhibits high tolerance to possible variations in visual appearance due to slide rotations, scaling and illumination changes. Experimental results indicate high reliability of the algorithm View full abstract»

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  • A meta-analysis of the training effectiveness of virtual reality surgical simulators

    Page(s): 51 - 58
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    The increasing use of virtual reality (VR) simulators in surgical training makes it imperative that definitive studies be performed to assess their training effectiveness. Indeed, in this paper we report the meta-analysis of the efficacy of virtual reality simulators in: 1) the transference of skills from the simulator training environment to the operating room, and 2) their ability to discriminate between the experience levels of their users. The task completion time and the error score were the two study outcomes collated and analyzed in this meta-analysis. Sixteen studies were identified from a computer-based literature search (1996-2004). The meta-analysis of the random effects model (because of the heterogeneity of the data) revealed that training on virtual reality simulators did lessen the time taken to complete a given surgical task as well as clearly differentiate between the experienced and the novice trainees. Meta-analytic studies such as the one reported here would be very helpful in the planning and setting up of surgical training programs and for the establishment of reference `learning curves' for a specific simulator and surgical task. If any such programs already exist, they can then indicate the improvements to be made in the simulator used, such as providing for more variety in their case scenarios based on the state and/or rate of learning of the trainee View full abstract»

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  • Computer-aided kidney segmentation on abdominal CT images

    Page(s): 59 - 65
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    In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation View full abstract»

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  • The design of an Internet-based system to maintain home monitoring adherence by lung transplant recipients

    Page(s): 66 - 76
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    Regimen adherence is a key factor in the success of home monitoring of lung transplant recipients. Patients generally adhere satisfactorily with home spirometry in the short-term, but adherence tends to decline over time. Telehealth and Internet technology provide new methods to address this issue. The unique contribution of the Adherence Enhancement Internet Program (AEIP) is the integration of multiple adherence enhancement strategies operating in a unified approach to the adherence problem, while meshing all user groups to facilitate interactions. This Internet-based program focused on promoting subject specific strategies was developed to maintain the initial high levels of adherence beyond the first year post transplant. The program provides more immediate subject feedback related to home monitoring data, reminders from the patient's health care providers, educational material, and guidance in dealing with subject specific barriers to maintaining adherence. It also simplifies communication between patients and health care providers, and supports providers in certain patient care tasks. A feasibility trial involving 12 lung transplant recipients demonstrated that subjects were able to use the AEIP with little training, found it acceptable, and were generally enthusiastic regarding it as a tool to maintain or enhance adherence View full abstract»

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  • Wavelet-based low-delay ECG compression algorithm for continuous ECG transmission

    Page(s): 77 - 83
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    The delay performance of compression algorithms is particularly important when time-critical data transmission is required. In this paper, we propose a wavelet-based electrocardiogram (ECG) compression algorithm with a low delay property for instantaneous, continuous ECG transmission suitable for telecardiology applications over a wireless network. The proposed algorithm reduces the frame size as much as possible to achieve a low delay, while maintaining reconstructed signal quality. To attain both low delay and high quality, it employs waveform partitioning, adaptive frame size adjustment, wavelet compression, flexible bit allocation, and header compression. The performances of the proposed algorithm in terms of reconstructed signal quality, processing delay, and error resilience were evaluated using the Massachusetts Institute of Technology University and Beth Israel Hospital (MIT-BIH) and Creighton University Ventricular Tachyarrhythmia (CU) databases and a code division multiple access-based simulation model with mobile channel noise View full abstract»

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  • A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography

    Page(s): 84 - 91
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (277 KB) |  | HTML iconHTML  

    A support vector machines (SVM) classifier was used to assess the severity of idiopathic scoliosis (IS) based on surface topographic images of human backs. Scoliosis is a condition that involves abnormal lateral curvature and rotation of the spine that usually causes noticeable trunk deformities. Based on the hypothesis that combining surface topography and clinical data using a SVM would produce better assessment results, we conducted a study using a dataset of 111 IS patients. Twelve surface and clinical indicators were obtained for each patient. The result of testing on the dataset showed that the system achieved 69-85% accuracy in testing. It outperformed a linear discriminant function classifier and a decision tree classifier on the dataset View full abstract»

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  • A model for the measurement of patient activity in a hospital suite

    Page(s): 92 - 99
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    At the time of hospitalization, it is essential to evaluate the general health status of a patient and to follow up the trends during therapy. Our work is focused on a set of tools for the measurement of patient activity. In this paper, we propose a few indicators of the patient activities of daily living, such as mobility, agitation, repartitions of stays, and displacements. As a result of this work, a diagnostic system was developed that could lead to a deeper knowledge of human activity rhythms in normal situations View full abstract»

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  • Aggregating automatically extracted regulatory pathway relations

    Page(s): 100 - 108
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (471 KB) |  | HTML iconHTML  

    Automatic tools to extract information from biomedical texts are needed to help researchers leverage the vast and increasing body of biomedical literature. While several biomedical relation extraction systems have been created and tested, little work has been done to meaningfully organize the extracted relations. Organizational processes should consolidate multiple references to the same objects over various levels of granularity, connect those references to other resources, and capture contextual information. We propose a feature decomposition approach to relation aggregation to support a five-level aggregation framework. Our BioAggregate tagger uses this approach to identify key features in extracted relation name strings. We show encouraging feature assignment accuracy and report substantial consolidation in a network of extracted relations View full abstract»

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  • A support method for the contextual interpretation of biomechanical data

    Page(s): 109 - 118
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    In the clinical field, biomechanical data provided by advanced technical devices are still underexploited. Data analysis usually consists of extracting attributes or computing synthetic values from temporal data and exploiting them by means of a monovariable statistical method. This article proposes a method to support clinicians, especially those in orthopedics, in the contextual interpretation of biomechanical data. We propose to characterize temporal biomechanical data by means of fuzzy space-time windows and to induce fuzzy decision trees to map the biomechanical and clinical data related to patients. Then, we present a method for objectively explaining a given clinical characteristic of a particular patient; this method is derived using the fuzzy rule base generated from the trees and a satisfiability measure. We have applied our method to real data in order to provide an objective explanation of the subjective self-evaluation of the functional status of patients with a shoulder prosthesis, and evaluate it by means of the stratified tenfold cross validation method. The mean explanation rate-which corresponds to the mean proportion of the patients belonging to test sets whose functional state is explained by the proposed method-exceeds 80% for more than half of the decision trees, and exceeds 70% for 94% of the trees. By supporting clinicians during the biomechanical data interpretation process, our method helps them take the objective biomechanical measurements in the medical practice into account, particularly in orthopedics. It can also make subjective evaluations more objective by mapping subjective and objective data View full abstract»

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  • Activity classification using realistic data from wearable sensors

    Page(s): 119 - 128
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (399 KB) |  | HTML iconHTML  

    Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network View full abstract»

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  • Hybrid retinal image registration

    Page(s): 129 - 142
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    This work studies retinal image registration in the context of the National Institutes of Health (NIH) Early Treatment Diabetic Retinopathy Study (ETDRS) standard. The ETDRS imaging protocol specifies seven fields of each retina and presents three major challenges for the image registration task. First, small overlaps between adjacent fields lead to inadequate landmark points for feature-based methods. Second, the non-uniform contrast/intensity distributions due to imperfect data acquisition will deteriorate the performance of area-based techniques. Third, high-resolution images contain large homogeneous nonvascular/texureless regions that weaken the capabilities of both feature-based and area-based techniques. In this work, we propose a hybrid retinal image registration approach for ETDRS images that effectively combines both area-based and feature-based methods. Four major steps are involved. First, the vascular tree is extracted by using an efficient local entropy-based thresholding technique. Next, zeroth-order translation is estimated by maximizing mutual information based on the binary image pair (area-based). Then image quality assessment regarding the ETDRS field definition is performed based on the translation model. If the image pair is accepted, higher-order transformations will be involved. Specifically, we use two types of features, landmark points and sampling points, for affine/quadratic model estimation. Three empirical conditions are derived experimentally to control the algorithm progress, so that we can achieve the lowest registration error and the highest success rate. Simulation results on 504 pairs of ETDRS images show the effectiveness and robustness of the proposed algorithm View full abstract»

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  • Artificial intelligence techniques for monitoring dangerous infections

    Page(s): 143 - 155
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    The monitoring and detection of nosocomial infections is a very important problem arising in hospitals. A hospital-acquired or nosocomial infection is a disease that develops after admission into the hospital and it is the consequence of a treatment, not necessarily a surgical one, performed by the medical staff. Nosocomial infections are dangerous because they are caused by bacteria which have dangerous (critical) resistance to antibiotics. This problem is very serious all over the world. In Italy, almost 5-8% of the patients admitted into hospitals develop this kind of infection. In order to reduce this figure, policies for controlling infections should be adopted by medical practitioners. In order to support them in this complex task, we have developed a system, called MERCURIO, capable of managing different aspects of the problem. The objectives of this system are the validation of microbiological data and the creation of a real time epidemiological information system. The system is useful for laboratory physicians, because it supports them in the execution of the microbiological analyses; for clinicians, because it supports them in the definition of the prophylaxis, of the most suitable antibiotic therapy and in monitoring patients' infections; and for epidemiologists, because it allows them to identify outbreaks and to study infection dynamics. In order to achieve these objectives, we have adopted expert system and data mining techniques. We have also integrated a statistical module that monitors the diffusion of nosocomial infections over time in the hospital, and that strictly interacts with the knowledge based module. Data mining techniques have been used for improving the system knowledge base. The knowledge discovery process is not antithetic, but complementary to the one based on manual knowledge elicitation. In order to verify the reliability of the tasks performed by MERCURIO and the usefulness of the knowledge discovery approach, we performed a test - - based on a dataset of real infection events. In the validation task MERCURIO achieved an accuracy of 98.5%, a sensitivity of 98.5% and a specificity of 99%. In the therapy suggestion task, MERCURIO achieved very high accuracy and specificity as well. The executed test provided many insights to experts, too (we discovered some of their mistakes). The knowledge discovery approach was very effective in validating part of the MERCURIO knowledge base, and also in extending it with new validation rules, confirmed by interviewed microbiologists and specific to the hospital laboratory under consideration View full abstract»

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  • Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring

    Page(s): 156 - 167
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    The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence View full abstract»

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  • Incorporating the sense of smell into patient and haptic surgical simulators

    Page(s): 168 - 173
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    It is widely recognized that the sense of smell plays an important role in the field of medicine. The sense of smell not only assists the physician in the diagnosis of certain disorders, but it also plays a surgical role as well. Historically, learning this skill was mostly contingent upon some level of clinical exposure to medically related odors. The advent of computerized scent production devices could change this. This article proposes a surgical simulation model that incorporates olfactory technologies into existing patient and haptic surgical simulators. If incorporated into virtual educational settings such as these, computerized scent production devices could be used not only as a novel way to enhance the virtual experience, but also as a way for medical students to begin to recognize the important role that the sense of smell plays during both diagnosis and surgery View full abstract»

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  • Tracking of left ventricular long axis from real-time three-dimensional echocardiography using optical flow techniques

    Page(s): 174 - 181
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    Two-dimensional echocardiography (2DE) is routinely used in clinical practice to measure left ventricular (LV) mass, dimensions, and function. The reliability of these measurements is highly dependent on the ability to obtain nonforeshortened long axis (LA) images of the left ventricle from transthoracic apical acoustic windows. Real time three-dimensional echocardiography (RT3DE) is a novel imaging technique that allows the acquisition of dynamic pyramidal data structures encompassing the entire ventricle and could potentially overcome the effects of LA foreshortening. Accordingly, the aim of this paper was to develop a nearly automated method based on optical flow techniques for the measurement of the left ventricular (LV) LA throughout the cardiac cycle from RT3DE data. The LV LA measurements obtained with the automated technique has been compared with LA measurements derived from manual selection of the LA from a volumetric display of RT3DE data. High correlation (r=.99,SEE=1.8%,y=.94x+5.3), no significant bias (-0.18 mm), and narrow limits of agreement (SD: 1.91 mm) were found. The comparison between the LA length derived from 2DE and RT3DE data showed significant underestimation of the 2DE based measurements. In conclusion, this study proves that RT3DE data overcome the effects of foreshortening and indicates that the method we propose allows fast and accurate quantification of LA length throughout the cardiac cycle View full abstract»

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  • Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure

    Page(s): 182 - 191
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    Electrocardiograph (ECG) compression techniques are gaining momentum due to the huge database requirements and wide band communication channels needed to maintain high quality ECG transmission. Advances in computer software and hardware enable the birth of new techniques in ECG compression, aiming at high compression rates. In general, most of the introduced ECG compression techniques depend on their evaluation performance on either inaccurate measures or measures targeting random behavior of error. In this paper, a new wavelet-based quality measure is proposed. A new wavelet-based quality measure is proposed. The new approach is based on decomposing the segment of interest into frequency bands where a weighted score is given to the band depending on its dynamic range and its diagnostic significance. A performance evaluation of the measure is conducted quantitatively and qualitatively. Comparative results with existing quality measures show that the new measure is insensitive to error variation, is accurate, and correlates very well with subjective tests View full abstract»

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  • Impact of monitoring technology in assisted living: outcome pilot

    Page(s): 192 - 198
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    This paper describes a study designed to assess the acceptance and some psychosocial impacts of monitoring technology in assisted living. Monitoring systems were installed in 22 assisted living units to track the activities of daily living (ADLs) and key alert conditions of residents (15 of whom were nonmemory care residents). Activity reports and alert notifications were sent to professional caregivers who provided care to residents participating in the study. Diagnostic use of the monitoring data was assessed. Nonmemory care residents were surveyed and assessed using the Satisfaction With Life Scale (SWLS) instrument. Pre- and post-installation SWLS scores were compared. Older adult participants accepted monitoring. The results suggest that monitoring technologies could provide care coordination tools that are accepted by residents and may have a positive impact on their quality of life 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.

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