Showing papers in "Journal of Medical Systems in 2012"

A Comprehensive Survey of Wireless Body Area Networks

Abstract: Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted.

Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications

Abstract: The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.

Smartphone App Use Among Medical Providers in ACGME Training Programs

Abstract: The past decade has witnessed the advent of the smartphone, a device armed with computing power, mobility and downloadable "apps," that has become commonplace within the medical field as both a personal and professional tool. The popularity of medically-related apps suggests that physicians use mobile technology to assist with clinical decision making, yet usage patterns have never been quantified. A digital survey examining smartphone and associated app usage was administered via email to all ACGME training programs. Data regarding respondent specialty, level of training, use of smartphones, use of smartphone apps, desired apps, and commonly used apps were collected and analyzed. Greater than 85% of respondents used a smartphone, of which the iPhone was the most popular (56%). Over half of the respondents reported using apps in their clinical practice; the most commonly used app types were drug guides (79%), medical calculators (18%), coding and billing apps (4%) and pregnancy wheels (4%). The most frequently requested app types were textbook/reference materials (average response: 55%), classification/treatment algorithms (46%) and general medical knowledge (43%). The clinical use of smartphones and apps will likely continue to increase, and we have demonstrated an absence of high-quality and popular apps despite a strong desire among physicians and trainees. This information should be used to guide the development of future healthcare delivery systems; expanded app functionality is almost certain but reliability and ease of use will likely remain major factors in determining the successful integration of apps into clinical practice.

Data Mining in Healthcare and Biomedicine: A Survey of the Literature

Abstract: As a new concept that emerged in the middle of 1990's, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare knowledge for clinical and administrative decision making as well as generate scientific hypotheses from large experimental data, clinical databases, and/or biomedical literature. This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields. A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented. Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry. Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields. A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health conditions and a disease, relationships among diseases, and relationships among drugs. The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals.

Systematic Review of Factors Influencing the Adoption of Information and Communication Technologies by Healthcare Professionals

Abstract: This systematic review of mixed methods studies focuses on factors that can facilitate or limit the implementation of information and communication technologies (ICTs) in clinical settings. Systematic searches of relevant bibliographic databases identified studies about interventions promoting ICT adoption by healthcare professionals. Content analysis was performed by two reviewers using a specific grid. One hundred and one (101) studies were included in the review. Perception of the benefits of the innovation (system usefulness) was the most common facilitating factor, followed by ease of use. Issues regarding design, technical concerns, familiarity with ICT, and time were the most frequent limiting factors identified. Our results suggest strategies that could effectively promote the successful adoption of ICT in healthcare professional practices.

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

Abstract: Breast cancer is a leading cause of death nowadays in women throughout the world In developed countries, it is the most common type of cancer in women, and it is the second or third most common malignancy in developing countries The cancer incidence is gradually increasing and remains a significant public health concern The limitations of mammography as a screening and diagnostic modality, especially in young women with dense breasts, necessitated the development of novel and more effective strategies with high sensitivity and specificity Thermal imaging (thermography) is a noninvasive imaging procedure used to record the thermal patterns using Infrared (IR) camera The aim of this study is to evaluate the feasibility of using thermal imaging as a potential tool for detecting breast cancer In this work, we have used 50 IR breast images (25 normal and 25 cancerous) collected from Singapore General Hospital, Singapore Texture features were extracted from co-occurrence matrix and run length matrix Subsequently, these features were fed to the Support Vector Machine (SVM) classifier for automatic classification of normal and malignant breast conditions Our proposed system gave an accuracy of 8810%, sensitivity and specificity of 8571% and 9048% respectively

Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review

Abstract: Diabetes is a chronic end organ disease that occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. Over time, diabetes affects the circulatory system, including that of the retina. Diabetic retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. In this paper we review algorithms used for the extraction of these features from digital fundus images. Furthermore, we discuss systems that use these features to classify individual fundus images. The classifications efficiency of different DR systems is discussed. Most of the reported systems are highly optimized with respect to the analyzed fundus images, therefore a generalization of individual results is difficult. However, this review shows that the classification results improved has improved recently, and it is getting closer to the classification capabilities of human ophthalmologists.

The Adoption and Implementation of RFID Technologies in Healthcare: A Literature Review

Abstract: Radio Frequency Identification (RFID) technology not only offers tracking capability to locate equipment, supplies and people in real time, but also provides efficient and accurate access to medical data for health professionals. However, the reality of RFID adoption in healthcare is far behind earlier expectation. This study reviews literature on the use of RFID in healthcare/hospitals following a formal innovation-decision framework. We aim to identify the common applications, potential benefits, barriers, and critical success factors. Our study facilitates quick assessment and provides guidance for researchers and practitioners in adopting RFID in medical arenas. Many earlier adopters in healthcare found RFID to be functional and useful in such areas as asset tracking and patient identification. Major barriers to adoption include technological limitations, interference concerns, prohibitive costs, lack of global standards and privacy concerns. Better designed RFID systems with low cost and privacy issues addressed are needed to increase acceptance of RFID in healthcare.

A More Secure Authentication Scheme for Telecare Medicine Information Systems

Abstract: It is important to guarantee the privacy and the security of the users in the telecare medicine information system. Recently, Wu et al.'s proposed an authentication scheme for mobile devices in telecare medicine information system. They added the pre-computing idea within the communication process to avoid the time-consuming exponential computations. They also claimed their scheme can withstand various attacks. We will show that their scheme suffers from the impersonation attack to the insider's attack. In order to overcome the weaknesses, we propose an improved scheme to eliminate the weakness. Our scheme is not only more secure than Wu et al.'s scheme, but also has better performance. Then our scheme is more efficient and appropriate to collocating with low power mobile devices for the telecare medicine information system.

A Secure Authentication Scheme for Telecare Medicine Information Systems

Abstract: The telecare medicine information system enables or supports health-care delivery services. In recent years, the increased availability of lower-cost telecommunications systems and custom made physiological monitoring devices for patients have made it possible to bring the advantages of telemedicine directly into the patient's home. These systems are moving towards an environment where automated patient medical records and electronically interconnected telecare facilities are prevalent. A secure authentication scheme will thus be needed to safeguard data integrity, confidentiality, and availability. Many schemes based on cryptography have been proposed for the goals. However, much of the schemes are vulnerable to various attacks, and are neither efficient, nor user friendly. Specially, in terms of efficiency, some schemes need the exponential computation resulting in high time cost. Therefore, we propose a novel authentication scheme that is added the pre-computing idea within the communication process to avoid the time-consuming exponential computations. Finally, it is shown to be more secure and practical for telecare medicine environments.

An Improved Authentication Scheme for Telecare Medicine Information Systems

Abstract: The telecare medicine information system enables or supports health-care delivery services. In order to safeguard patients' privacy, such as telephone number, medical record number, health information, etc., a secure authentication scheme will thus be in demand. Recently, Wu et al. proposed a smart card based password authentication scheme for the telecare medicine information system. Later, He et al. pointed out that Wu et al.'s scheme could not resist impersonation attacks and insider attacks, and then presented a new scheme. In this paper, we show that both of them fail to achieve two-factor authentication as smart card based password authentication schemes should achieve. We also propose an improved authentication scheme for the telecare medicine information system, and demonstrate that the improved one satisfies the security requirements of two-factor authentication and is also efficient.

Detection and Localization of Myocardial Infarction using K-nearest Neighbor Classifier

Abstract: This paper presents automatic detection and localization of myocardial infarction (MI) using K-nearest neighbor (KNN) classifier. Time domain features of each beat in the ECG signal such as T wave amplitude, Q wave and ST level deviation, which are indicative of MI, are extracted from 12 leads ECG. Detection of MI aims to classify normal subjects without myocardial infarction and subjects suffering from Myocardial Infarction. For further investigation, Localization of MI is done to specify the region of infarction of the heart. Total 20,160 ECG beats from PTB database available on Physio-bank is used to investigate the performance of extracted features with KNN classifier. In the case of MI detection, sensitivity and specificity of KNN is found to be 99.9% using half of the randomly selected beats as training set and rest of the beats for testing. Moreover, Arif-Fayyaz pruning algorithm is used to prune the data which will reduce the storage requirement and computational cost of search. After pruning, sensitivity and specificity are dropped to 97% and 99.6% respectively but training is reduced by 93%. Myocardial Infarction beats are divided into ten classes based on the location of the infarction along with one class of normal subjects. Sensitivity and Specificity of above 90% is achieved for all eleven classes with overall classification accuracy of 98.8%. Some of the ECG beats are misclassified but interestingly these are misclassified to those classes whose location of infarction is near to the true classes of the ECG beats. Pruning is done on the training set for eleven classes and training set is reduced by 70% and overall classification accuracy of 98.3% is achieved. The proposed method due to its simplicity and high accuracy over the PTB database can be very helpful in correct diagnosis of MI in a practical scenario.

An Efficient Authentication Scheme for Telecare Medicine Information Systems

Abstract: To ensure patients' privacy, such as telephone number, medical record number, health information, etc., authentication schemes for telecare medicine information systems (TMIS) have been studied widely. Recently, Wei et al. proposed an efficient authentication scheme for TMIS. They claimed their scheme could resist various attacks. However, in this paper, we will show their scheme is vulnerable to an off-line password guessing attack when user's smart card is lost. To improve the security, we propose a new authentication scheme for TMIS. The analysis shows our scheme could overcome the weaknesses in Wei et al.'s scheme and has better performance than their scheme.

An Efficient and Secure Dynamic ID-based Authentication Scheme for Telecare Medical Information Systems

Abstract: The rapidly increased availability of always-on broadband telecommunication environments and lower-cost vital signs monitoring devices bring the advantages of telemedicine directly into the patient's home. Hence, the control of access to remote medical servers' resources has become a crucial challenge. A secure authentication scheme between the medical server and remote users is therefore needed to safeguard data integrity, confidentiality and to ensure availability. Recently, many authentication schemes that use low-cost mobile devices have been proposed to meet these requirements. In contrast to previous schemes, Khan et al. proposed a dynamic ID-based remote user authentication scheme that reduces computational complexity and includes features such as a provision for the revocation of lost or stolen smart cards and a time expiry check for the authentication process. However, Khan et al.'s scheme has some security drawbacks. To remedy theses, this study proposes an enhanced authentication scheme that overcomes the weaknesses inherent in Khan et al.'s scheme and demonstrated this scheme is more secure and robust for use in a telecare medical information system.

Barriers to Physicians' Adoption of Healthcare Information Technology: An Empirical Study on Multiple Hospitals

Abstract: Prior research on technology usage had largely overlooked the issue of user resistance or barriers to technology acceptance. Prior research on the Electronic Medical Records had largely focused on technical issues but rarely on managerial issues. Such oversight prevented a better understanding of users' resistance to new technologies and the antecedents of technology rejection. Incorporating the enablers and the inhibitors of technology usage intention, this study explores physicians' reactions towards the electronic medical record. The main focus is on the barriers, perceived threat and perceived inequity. 115 physicians from 6 hospitals participated in the questionnaire survey. Structural Equation Modeling was employed to verify the measurement scale and research hypotheses. According to the results, perceived threat shows a direct and negative effect on perceived usefulness and behavioral intentions, as well as an indirect effect on behavioral intentions via perceived usefulness. Perceived inequity reveals a direct and positive effect on perceived threat, and it also shows a direct and negative effect on perceived usefulness. Besides, perceived inequity reveals an indirect effect on behavioral intentions via perceived usefulness with perceived threat as the inhibitor. The research finding presents a better insight into physicians' rejection and the antecedents of such outcome. For the healthcare industry understanding the factors contributing to physicians' technology acceptance is important as to ensure a smooth implementation of any new technology. The results of this study can also provide change managers reference to a smooth IT introduction into an organization. In addition, our proposed measurement scale can be applied as a diagnostic tool for them to better understand the status quo within their organizations and users' reactions to technology acceptance. By doing so, barriers to physicians' acceptance can be identified earlier and more effectively before leading to technology rejection.

An Ultra Low-power and Traffic-adaptive Medium Access Control Protocol for Wireless Body Area Network

Abstract: Wireless Body Area Network (WBAN) consists of low-power, miniaturized, and autonomous wireless sensor nodes that enable physicians to remotely monitor vital signs of patients and provide real-time feedback with medical diagnosis and consultations. It is the most reliable and cheaper way to take care of patients suffering from chronic diseases such as asthma, diabetes and cardiovascular diseases. Some of the most important attributes of WBAN is low-power consumption and delay. This can be achieved by introducing flexible duty cycling techniques on the energy constraint sensor nodes. Stated otherwise, low duty cycle nodes should not receive frequent synchronization and control packets if they have no data to send/receive. In this paper, we introduce a Traffic-adaptive MAC protocol (TaMAC) by taking into account the traffic information of the sensor nodes. The protocol dynamically adjusts the duty cycle of the sensor nodes according to their traffic-patterns, thus solving the idle listening and overhearing problems. The traffic-patterns of all sensor nodes are organized and maintained by the coordinator. The TaMAC protocol is supported by a wakeup radio that is used to accommodate emergency and on-demand events in a reliable manner. The wakeup radio uses a separate control channel along with the data channel and therefore it has considerably low power consumption requirements. Analytical expressions are derived to analyze and compare the performance of the TaMAC protocol with the well-known beacon-enabled IEEE 802.15.4 MAC, WiseMAC, and SMAC protocols. The analytical derivations are further validated by simulation results. It is shown that the TaMAC protocol outperforms all other protocols in terms of power consumption and delay.

A Secure EHR System Based on Hybrid Clouds

Abstract: Consequently, application services rendering remote medical services and electronic health record (EHR) have become a hot topic and stimulating increased interest in studying this subject in recent years. Information and communication technologies have been applied to the medical services and healthcare area for a number of years to resolve problems in medical management. Sharing EHR information can provide professional medical programs with consultancy, evaluation, and tracing services can certainly improve accessibility to the public receiving medical services or medical information at remote sites. With the widespread use of EHR, building a secure EHR sharing environment has attracted a lot of attention in both healthcare industry and academic community. Cloud computing paradigm is one of the popular healthIT infrastructures for facilitating EHR sharing and EHR integration. In this paper, we propose an EHR sharing and integration system in healthcare clouds and analyze the arising security and privacy issues in access and management of EHRs.

Wireless Body Area Network (WBAN) Design Techniques and Performance Evaluation

Abstract: In recent years interest in the application of Wireless Body Area Network (WBAN) for patient monitoring applications has grown significantly. A WBAN can be used to develop patient monitoring systems which offer flexibility to medical staff and mobility to patients. Patients monitoring could involve a range of activities including data collection from various body sensors for storage and diagnosis, transmitting data to remote medical databases, and controlling medical appliances, etc. Also, WBANs could operate in an interconnected mode to enable remote patient monitoring using telehealth/e-health applications. A WBAN can also be used to monitor athletes' performance and assist them in training activities. For such applications it is very important that a WBAN collects and transmits data reliably, and in a timely manner to a monitoring entity. In order to address these issues, this paper presents WBAN design techniques for medical applications. We examine the WBAN design issues with particular emphasis on the design of MAC protocols and power consumption profiles of WBAN. Some simulation results are presented to further illustrate the performances of various WBAN design techniques.

An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters

Abstract: Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy.

Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions

Abstract: Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that--(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians' utilization of these decision aids.

A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases

Abstract: Listening via stethoscope is a primary method, being used by physicians for distinguishing normally and abnormal cardiac systems. Listening to the voices, coming from the cardiac valves via stethoscope, upon the flow of the blood running in the heart, physicians examine whether there is any abnormality with regard to the heart. However, listening via stethoscope has got a number of limitations, for interpreting different heart sounds depends on hearing ability, experience, and respective skill of the physician. Such limitations may be reduced by developing biomedical based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary and mitral stenosis heart valve diseases via stethoscope. Developed system was mainly comprised of three stages, namely as being feature extraction, dimension reduction, and classification. At feature extraction stage, applying Discrete Fourier Transform (DFT) and Burg autoregressive (AR) spectrum analysis method, features, representing heart sounds in frequency domain, were obtained. Obtained features were reduced in lower dimensions via Principal Component Analysis (PCA), being used as a dimension reduction technique. Heart sounds were classified by having the features applied as input to Artificial Neural Network (ANN). Classification results have shown that, dimension reduction, being conducted via PCA, has got positive effects on the classification of the heart sounds.

Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval

Abstract: A new algorithm for medical image retrieval is presented in the paper. An 8-bit grayscale image is divided into eight binary bit-planes, and then binary wavelet transform (BWT) which is similar to the lifting scheme in real wavelet transform (RWT) is performed on each bitplane to extract the multi-resolution binary images. The local binary pattern (LBP) features are extracted from the resultant BWT sub-bands. Three experiments have been carried out for proving the effectiveness of the proposed algorithm. Out of which two are meant for medical image retrieval and one for face retrieval. It is further mentioned that the database considered for three experiments are OASIS magnetic resonance imaging (MRI) database, NEMA computer tomography (CT) database and PolyU-NIRFD face database. The results after investigation shows a significant improvement in terms of their evaluation measures as compared to LBP and LBP with Gabor transform.

Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound

Abstract: Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.

Image Analysis Tools for Evaluation of Microscopic Views of Immunohistochemically Stained Specimen in Medical Research---a Review

Abstract: The aim of this study is to review the methods being used for image analysis of microscopic views of immunohistochemically stained specimen in medical research. The solutions available range from general purpose software to commercial packages. Many studies have developed their own custom written programs based on some general purpose software available. Many groups have reported development of computer aided image analysis programs aiming at obtaining faster, simpler and cheaper solutions. Image analysis tools namely Aperio, Lucia, Metaview, Metamorph, ImageJ, Scion, Adobe Photoshop, Image Pro Plus are also used for evaluation of expressions using immunohistochemical staining. An overview of such methods used for image analysis is provided in this paper. This study concludes that there is good scope for development of freely available software for staining intensity quantification, which a medical researcher could easily use without requiring high level computer skills.

The Adoption of Mobile Health Management Services: An Empirical Study

Abstract: As their populations age, many countries are facing the increasing economic pressure of providing healthcare to their people. In Taiwan, this problem is exacerbated by an increasing rate of obesity and obesity-related conditions. Encouraging the adoption of personal health management services is one way to maintain current levels of personal health and to efficiently manage the distribution of healthcare resources. This study introduces Mobile Health Management Services (MHMS) and employs the Technology Acceptance Model (TAM) to explore the intention of students in Executive Master of Business Management programs to adopt mobile health management technology. Partial least squares (PLS) was used to analyze the collected data, and the results revealed that "perceived usefulness" and "attitude" significantly affected the behavioral intention of adopting MHMS. Both "perceived ease of use" and "perceived usefulness," significantly affected "attitude," and "perceived ease of use" significantly affected "perceived usefulness" as well. The results also show that the determinants of intention toward MHMS differed with age; young adults had higher intention to adopt MHMS to manage their personal health. Therefore, relevant governmental agencies may profitably promote the management of personal health among this population. Successful promotion of personal health management will contribute to increases in both the level of general health and the efficient management of healthcare resources.

Diagnosis of Diabetes Diseases Using an Artificial Immune Recognition System2 (AIRS2) with Fuzzy K-nearest Neighbor

Abstract: The use of expert systems and artificial intelligence techniques in disease diagnosis has been increasing gradually. Artificial Immune Recognition System (AIRS) is one of the methods used in medical classification problems. AIRS2 is a more efficient version of the AIRS algorithm. In this paper, we used a modified AIRS2 called MAIRS2 where we replace the K- nearest neighbors algorithm with the fuzzy K-nearest neighbors to improve the diagnostic accuracy of diabetes diseases. The diabetes disease dataset used in our work is retrieved from UCI machine learning repository. The performances of the AIRS2 and MAIRS2 are evaluated regarding classification accuracy, sensitivity and specificity values. The highest classification accuracy obtained when applying the AIRS2 and MAIRS2 using 10-fold cross-validation was, respectively 82.69% and 89.10%.

Detection of Abnormalities for Diagnosing of Children with Autism Disorders Using of Quantitative Electroencephalography Analysis

Abstract: Quantitative electroencephalography (qEEG) has been used as a tool for neurophysiologic diagnostic. We used spectrogram and coherence values for evaluating qEEG in 17 children (13 boys and 4 girls aged between 6 and 11) with autism disorders (ASD) and 11 control children (7 boys and 4 girls with the same age range). Evaluation of qEEG with statistical analysis demonstrated that alpha frequency band (8---13 Hz) had the best distinction level of 96.4% in relaxed eye-opened condition using spectrogram criteria. The ASD group had significant lower spectrogram criteria values in left brain hemisphere, (p?

SVM feature selection based rotation forest ensemble classifiers to improve computer-aided diagnosis of Parkinson disease.

Abstract: Parkinson disease (PD) is an age-related deterioration of certain nerve systems, which affects movement, balance, and muscle control of clients. PD is one of the common diseases which affect 1% of people older than 60 years. A new classification scheme based on support vector machine (SVM) selected features to train rotation forest (RF) ensemble classifiers is presented for improving diagnosis of PD. The dataset contains records of voice measurements from 31 people, 23 with PD and each record in the dataset is defined with 22 features. The diagnosis model first makes use of a linear SVM to select ten most relevant features from 22. As a second step of the classification model, six different classifiers are trained with the subset of features. Subsequently, at the third step, the accuracies of classifiers are improved by the utilization of RF ensemble classification strategy. The results of the experiments are evaluated using three metrics; classification accuracy (ACC), Kappa Error (KE) and Area under the Receiver Operating Characteristic (ROC) Curve (AUC). Performance measures of two base classifiers, i.e. KStar and IBk, demonstrated an apparent increase in PD diagnosis accuracy compared to similar studies in literature. After all, application of RF ensemble classification scheme improved PD diagnosis in 5 of 6 classifiers significantly. We, numerically, obtained about 97% accuracy in RF ensemble of IBk (a K-Nearest Neighbor variant) algorithm, which is a quite high performance for Parkinson disease diagnosis.

Criteria for the Evaluation of a Cloud-Based Hospital Information System Outsourcing Provider

Abstract: As cloud computing technology has proliferated rapidly worldwide, there has been a trend toward adopting cloud-based hospital information systems (CHISs). This study examines the critical criteria for selecting the CHISs outsourcing provider. The fuzzy Delphi method (FDM) is used to evaluate the primary indicator collected from 188 useable responses at a working hospital in Taiwan. Moreover, the fuzzy analytic hierarchy process (FAHP) is employed to calculate the weights of these criteria and establish a fuzzy multi-criteria model of CHISs outsourcing provider selection from 42 experts. The results indicate that the five most critical criteria related to CHISs outsourcing provider selection are (1) system function, (2) service quality, (3) integration, (4) professionalism, and (5) economics. This study may contribute to understanding how cloud-based hospital systems can reinforce content design and offer a way to compete in the field by developing more appropriate systems.

Throughput and Delay Analysis of IEEE 802.15.6-based CSMA/CA Protocol

Abstract: The IEEE 802.15.6 is a new communication standard on Wireless Body Area Network (WBAN) that focuses on a variety of medical, Consumer Electronics (CE) and entertainment applications. In this paper, the throughput and delay performance of the IEEE 802.15.6 is presented. Numerical formulas are derived to determine the maximum throughput and minimum delay limits of the IEEE 802.15.6 for an ideal channel with no transmission errors. These limits are derived for different frequency bands and data rates. Our analysis is validated by extensive simulations using a custom C+?+ simulator. Based on analytical and simulation results, useful conclusions are derived for network provisioning and packet size optimization for different applications.