Showing papers in "Journal of Medical Systems in 2016"
TL;DR: An App (called Healthcare Data Gateway (HGD) architecture based on blockchain is proposed to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private.
Abstract: Healthcare data are a valuable source of healthcare intelligence. Sharing of healthcare data is one essential step to make healthcare system smarter and improve the quality of healthcare service. Healthcare data, one personal asset of patient, should be owned and controlled by patient, instead of being scattered in different healthcare systems, which prevents data sharing and puts patient privacy at risks. Blockchain is demonstrated in the financial field that trusted, auditable computing is possible using a decentralized network of peers accompanied by a public ledger. In this paper, we proposed an App (called Healthcare Data Gateway (HGD)) architecture based on blockchain to enable patient to own, control and share their own data easily and securely without violating privacy, which provides a new potential way to improve the intelligence of healthcare systems while keeping patient data private. Our proposed purpose-centric access model ensures patient own and control their healthcare data; simple unified Indicator-Centric Schema (ICS) makes it possible to organize all kinds of personal healthcare data practically and easily. We also point out that MPC (Secure Multi-Party Computing) is one promising solution to enable untrusted third-party to conduct computation over patient data without violating privacy.
884 citations
TL;DR: Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases.
Abstract: Public healthcare has been paid an increasing attention given the exponential growth human population and medical expenses It is well known that an effective health monitoring system can detect abnormalities of health conditions in time and make diagnoses according to the gleaned data As a vital approach to diagnose heart diseases, ECG monitoring is widely studied and applied However, nearly all existing portable ECG monitoring systems cannot work without a mobile application, which is responsible for data collection and display In this paper, we propose a new method for ECG monitoring based on Internet-of-Things (IoT) techniques ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi Both the HTTP and MQTT protocols are employed in the IoT cloud in order to provide visual and timely ECG data to users Nearly all smart terminals with a web browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue Experiments are carried out on healthy volunteers in order to verify the reliability of the entire system Experimental results reveal that the proposed system is reliable in collecting and displaying real-time ECG data, which can aid in the primary diagnosis of certain heart diseases
365 citations
TL;DR: The current state of mobile applications for health behavioural change with an emphasis on applications that promote physical activity is reviewed, and system credibility support was found to have only low levels of representation as a persuasive systems design feature in mobile application for supporting physical activity.
Abstract: Persuasive technology in mobile applications can be used to influence the behaviour of users. A framework known as the Persuasive Systems Design model has been developed for designing and evaluating systems that influence the attitudes or behaviours of users. This paper reviews the current state of mobile applications for health behavioural change with an emphasis on applications that promote physical activity. The inbuilt persuasive features of mobile applications were evaluated using the Persuasive Systems Design model. A database search was conducted to identify relevant articles. Articles were then reviewed using the Persuasive Systems Design model as a framework for analysis. Primary task support, dialogue support, and social support were found to be moderately represented in the selected articles. However, system credibility support was found to have only low levels of representation as a persuasive systems design feature in mobile applications for supporting physical activity. To ensure that available mobile technology resources are best used to improve the wellbeing of people, it is important that the design principles that influence the effectiveness of persuasive technology be understood.
182 citations
TL;DR: Policy makers should consider incentives that continue to reduce implementation cost, possibly aimed more directly at organizations that are known to have lower adoption rates, such as small hospitals in rural areas.
Abstract: Federal efforts and local initiatives to increase adoption and use of electronic health records (EHRs) continue, particularly since the enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act. Roughly one in four hospitals not adopted even a basic EHR system. A review of the barriers may help in understanding the factors deterring certain healthcare organizations from implementation. We wanted to assemble an updated and comprehensive list of adoption barriers of EHR systems in the United States. Authors searched CINAHL, MEDLINE, and Google Scholar, and accepted only articles relevant to our primary objective. Reviewers independently assessed the works highlighted by our search and selected several for review. Through multiple consensus meetings, authors tapered articles to a final selection most germane to the topic (n = 27). Each article was thoroughly examined by multiple authors in order to achieve greater validity. Authors identified 39 barriers to EHR adoption within the literature selected for the review. These barriers appeared 125 times in the literature; the most frequently mentioned barriers were regarding cost, technical concerns, technical support, and resistance to change. Despite federal and local incentives, the initial cost of adopting an EHR is a common existing barrier. The other most commonly mentioned barriers include technical support, technical concerns, and maintenance/ongoing costs. Policy makers should consider incentives that continue to reduce implementation cost, possibly aimed more directly at organizations that are known to have lower adoption rates, such as small hospitals in rural areas.
178 citations
TL;DR: This study presents a novel hybrid method for CAD diagnosis, including risk factor identification using correlation based feature subset (CFS) selection with particle swam optimization (PSO) search method and K-means clustering algorithms, which outperforms other techniques.
Abstract: Coronary artery disease (CAD) is caused by atherosclerosis in coronary arteries and results in cardiac arrest and heart attack. For diagnosis of CAD, angiography is used which is a costly time consuming and highly technical invasive method. Researchers are, therefore, prompted for alternative methods such as machine learning algorithms that could use noninvasive clinical data for the disease diagnosis and assessing its severity. In this study, we present a novel hybrid method for CAD diagnosis, including risk factor identification using correlation based feature subset (CFS) selection with particle swam optimization (PSO) search method and K-means clustering algorithms. Supervised learning algorithms such as multi-layer perceptron (MLP), multinomial logistic regression (MLR), fuzzy unordered rule induction algorithm (FURIA) and C4.5 are then used to model CAD cases. We tested this approach on clinical data consisting of 26 features and 335 instances collected at the Department of Cardiology, Indira Gandhi Medical College, Shimla, India. MLR achieves highest prediction accuracy of 88.4 %.We tested this approach on benchmarked Cleaveland heart disease data as well. In this case also, MLR, outperforms other techniques. Proposed hybridized model improves the accuracy of classification algorithms from 8.3 % to 11.4 % for the Cleaveland data. The proposed method is, therefore, a promising tool for identification of CAD patients with improved prediction accuracy.
161 citations
TL;DR: The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies and provides a high classification accuracy of 93 %.
Abstract: Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.
154 citations
TL;DR: The goal of this paper is to provide a state of the art review of recent medical simulators that use haptic devices and focuses on stitching, palpation, dental procedures, endoscopy, laparoscopy and orthopaedics.
Abstract: Medical procedures often involve the use of the tactile sense to manipulate organs or tissues by using special tools. Doctors require extensive preparation in order to perform them successfully; for example, research shows that a minimum of 750 operations are needed to acquire sufficient experience to perform medical procedures correctly. Haptic devices have become an important training alternative and they have been considered to improve medical training because they let users interact with virtual environments by adding the sense of touch to the simulation. Previous articles in the field state that haptic devices enhance the learning of surgeons compared to current training environments used in medical schools (corpses, animals, or synthetic skin and organs). Consequently, virtual environments use haptic devices to improve realism. The goal of this paper is to provide a state of the art review of recent medical simulators that use haptic devices. In particular we focus on stitching, palpation, dental procedures, endoscopy, laparoscopy, and orthopaedics. These simulators are reviewed and compared from the viewpoint of used technology, the number of degrees of freedom, degrees of force feedback, perceived realism, immersion, and feedback provided to the user. In the conclusion, several observations per area and suggestions for future work are provided.
151 citations
TL;DR: Results demonstrate that the proposed Random Forests classifier has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).
Abstract: In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of different statistical features were extracted from the obtained frequency bands to denote the distribution of wavelet coefficients. This study shows that RF classifier achieves superior performances compared to other decision tree methods using 10-fold cross-validation for the ECG datasets and the obtained results suggest that further significant improvements in terms of classification accuracy can be accomplished by the proposed classification system. Accurate ECG signal classification is the major requirement for detection of all arrhythmia types. Performances of the proposed system have been evaluated on two different databases, namely MIT-BIH database and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. For MIT-BIH database, RF classifier yielded an overall accuracy 99.33 % against 98.44 and 98.67 % for the C4.5 and CART classifiers, respectively. For St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, RF classifier yielded an overall accuracy 99.95 % against 99.80 % for both C4.5 and CART classifiers, respectively. The combined model with multiscale principal component analysis (MSPCA) de-noising, discrete wavelet transform (DWT) and RF classifier also achieves better performance with the area under the receiver operating characteristic (ROC) curve (AUC) and F-measure equal to 0.999 and 0.993 for MIT-BIH database and 1 and 0.999 for and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, respectively. Obtained results demonstrate that the proposed system has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).
147 citations
TL;DR: The telepresence robot system designed to improve the well-being of elderly by supporting them to do daily activities independently, to facilitate social interaction in order to overcome a sense of social isolation and loneliness as well as to support the professional caregivers in everyday care is described.
Abstract: In this paper we described the telepresence robot system designed to improve the well-being of elderly by supporting them to do daily activities independently, to facilitate social interaction in order to overcome a sense of social isolation and loneliness as well as to support the professional caregivers in everyday care. In order to investigate the acceptance of the developed robot system, evaluation study involved elderly people and professional caregivers, as two potential user groups was conducted. The results of this study are also presented and discussed.
138 citations
TL;DR: Preliminary data indicated that a high speed camera can be potentially utilized for unobtrusive contactless monitoring of abrupt blood pressure changes in a variety of settings and showed high intra-individual correlation between iPTT and BP.
Abstract: Recent studies demonstrated that blood pressure (BP) can be estimated using pulse transit time (PTT). For PTT calculation, photoplethysmogram (PPG) is usually used to detect a time lag in pulse wave propagation which is correlated with BP. Until now, PTT and PPG were registered using a set of body-worn sensors. In this study a new methodology is introduced allowing contactless registration of PTT and PPG using high speed camera resulting in corresponding image-based PTT (iPTT) and image-based PPG (iPPG) generation. The iPTT value can be potentially utilized for blood pressure estimation however extent of correlation between iPTT and BP is unknown. The goal of this preliminary feasibility study was to introduce the methodology for contactless generation of iPPG and iPTT and to make initial estimation of the extent of correlation between iPTT and BP "in vivo." A short cycling exercise was used to generate BP changes in healthy adult volunteers in three consecutive visits. BP was measured by a verified BP monitor simultaneously with iPTT registration at three exercise points: rest, exercise peak, and recovery. iPPG was simultaneously registered at two body locations during the exercise using high speed camera at 420 frames per second. iPTT was calculated as a time lag between pulse waves obtained as two iPPG's registered from simultaneous recoding of head and palm areas. The average inter-person correlation between PTT and iPTT was 0.85?±?0.08. The range of inter-person correlations between PTT and iPTT was from 0.70 to 0.95 (p?
123 citations
TL;DR: The results show that the proposed Real-time Medical Emergency Response System has the capability of efficiently processing WBAN sensory data from millions of users in order to perform real-time responses in case of emergencies.
Abstract: Healthy people are important for any nation's development. Use of the Internet of Things (IoT)-based body area networks (BANs) is increasing for continuous monitoring and medical healthcare in order to perform real-time actions in case of emergencies. However, in the case of monitoring the health of all citizens or people in a country, the millions of sensors attached to human bodies generate massive volume of heterogeneous data, called "Big Data." Processing Big Data and performing real-time actions in critical situations is a challenging task. Therefore, in order to address such issues, we propose a Real-time Medical Emergency Response System that involves IoT-based medical sensors deployed on the human body. Moreover, the proposed system consists of the data analysis building, called "Intelligent Building," depicted by the proposed layered architecture and implementation model, and it is responsible for analysis and decision-making. The data collected from millions of body-attached sensors is forwarded to Intelligent Building for processing and for performing necessary actions using various units such as collection, Hadoop Processing (HPU), and analysis and decision. The feasibility and efficiency of the proposed system are evaluated by implementing the system on Hadoop using an UBUNTU 14.04 LTS coreTMi5 machine. Various medical sensory datasets and real-time network traffic are considered for evaluating the efficiency of the system. The results show that the proposed system has the capability of efficiently processing WBAN sensory data from millions of users in order to perform real-time responses in case of emergencies.
TL;DR: There is a need for mobile apps for self-management of diabetes with more features in order to increase the number of long-term users and thus influence better self- management of the disease.
Abstract: Mobile applications (apps) can be very useful software on smartphones for all aspects of people's lives. Chronic diseases, such as diabetes, can be made manageable with the support of mobile apps. Applications on smartphones can also help people with diabetes to control their fitness and health. A systematic review of free apps in the English language for smartphones in three of the most popular mobile app stores: Google Play (Android), App Store (iOS) and Windows Phone Store, was performed from November to December 2015. The review of freely available mobile apps for self-management of diabetes was conducted based on the criteria for promoting diabetes self-management as defined by Goyal and Cafazzo (monitoring blood glucose level and medication, nutrition, physical exercise and body weight). The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) was followed. Three independent experts in the field of healthcare-related mobile apps were included in the assessment for eligibility and testing phase. We tested and evaluated 65 apps (21 from Google Play Store, 31 from App Store and 13 from Windows Phone Store). Fifty-six of these apps did not meet even minimal requirements or did not work properly. While a wide selection of mobile applications is available for self-management of diabetes, current results show that there are only nine (5 from Google Play Store, 3 from App Store and 1 from Windows Phone Store) out of 65 reviewed mobile apps that can be versatile and useful for successful self-management of diabetes based on selection criteria. The levels of inclusion of features based on selection criteria in selected mobile apps can be very different. The results of the study can be used as a basis to prvide app developers with certain recommendations. There is a need for mobile apps for self-management of diabetes with more features in order to increase the number of long-term users and thus influence better self-management of the disease.
TL;DR: Several personal-level factors were associated with having mHealth apps and their perceived helpfulness among their users, indicating a multidimensional digital divide in the population of US adults.
Abstract: This study explores the use of mobile health applications (mHealth apps) on smartphones or tablets for health-seeking behavior among US adults. Data was obtained from cycle 4 of the 4th edition of the Health Information National Trends Survey (HINTS 4). Weighted multivariate logistic regression models examined predictors of 1) having mHealth apps, 2) usefulness of mHealth apps in achieving health behavior goals, 3) helpfulness in medical care decision-making, and 4) asking a physician new questions or seeking a second opinion. Using the Andersen Model of health services utilization, independent variables of interest were grouped under predisposing factors (age, gender, race, ethnicity, and marital status), enabling factors (education, employment, income, regular provider, health insurance, and rural/urban location of residence), and need factors (general health, confidence in their ability to take care of health, Body Mass Index, smoking status, and number of comorbidities). In a national sample of adults who had smartphones or tablets, 36 % had mHealth apps on their devices. Among those with apps, 60 % reported the usefulness of mHealth apps in achieving health behavior goals, 35 % reported their helpfulness for medical care decision-making, and 38 % reported their usefulness in asking their physicians new questions or seeking a second opinion. The multivariate models revealed that respondents were more likely to have mHealth apps if they had more education, health insurance, were confident in their ability to take good care of themselves, or had comorbidities, and were less likely to have them if they were older, had higher income, or lived in rural areas. In terms of usefulness of mHealth apps, those who were older and had higher income were less likely to report their usefulness in achieving health behavior goals. Those who were older, African American, and had confidence in their ability to take care of their health were more likely to respond that the mHealth apps were helpful in making a medical care decision and asking their physicians new questions or for a second opinion. Potentially, mHealth apps may reduce the burden on primary care, reduce costs, and improve the quality of care. However, several personal-level factors were associated with having mHealth apps and their perceived helpfulness among their users, indicating a multidimensional digital divide in the population of US adults.
TL;DR: This study resulted in the need to develop a maturity model based on a holistic approach that will include a comprehensive set of influencing factors to reach all areas and subsystems of health care organizations.
Abstract: The maturity models are instruments to facilitate organizational management, including the management of its information systems function. These instruments are used also in hospitals. The objective of this article is to identify and compare the maturity models for management of information systems and technologies (IST) in healthcare. For each maturity model, it is identified the methodology of development and validation, as well as the scope, stages and their characteristics by dimensions or influence factors. This study resulted in the need to develop a maturity model based on a holistic approach. It will include a comprehensive set of influencing factors to reach all areas and subsystems of health care organizations.
TL;DR: This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images by exploiting fractional Fourier entropy features extracted from each brain image to train the biases and weights of MLP.
Abstract: This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal hidden neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE?+?KC-MLP?+?ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods.
TL;DR: The investigation suggest that the performance of learners using mobile apps is statistical better than the students using the traditional method, however, mobile devices should be considered as an additional tool to complement the teachers’ explanation.
Abstract: The aim of this research is to contribute to the general system education providing new insights and resources. This study performs a quasi-experimental study at University of Salamanca with 30 students to compare results between using an anatomic app for learning and the formal traditional method conducted by a teacher. The findings of the investigation suggest that the performance of learners using mobile apps is statistical better than the students using the traditional method. However, mobile devices should be considered as an additional tool to complement the teachers' explanation and it is necessary to overcome different barriers and challenges to adopt these pedagogical methods at University.
TL;DR: A patient state recognition system for the healthcare framework is proposed in such a way that it provides good recognition accuracy, provides low-cost modeling, and is scalable.
Abstract: Smart, interactive healthcare is necessary in the modern age. Several issues, such as accurate diagnosis, low-cost modeling, low-complexity design, seamless transmission, and sufficient storage, should be addressed while developing a complete healthcare framework. In this paper, we propose a patient state recognition system for the healthcare framework. We design the system in such a way that it provides good recognition accuracy, provides low-cost modeling, and is scalable. The system takes two main types of input, video and audio, which are captured in a multi-sensory environment. Speech and video input are processed separately during feature extraction and modeling; these two input modalities are merged at score level, where the scores are obtained from the models of different patients' states. For the experiments, 100 people were recruited to mimic a patient's states of normal, pain, and tensed. The experimental results show that the proposed system can achieve an average 98.2 % recognition accuracy.
TL;DR: A novel framework for controlling access to EHRs stored in semi-trusted cloud servers (e.g. a private cloud or a community cloud) is proposed and the ciphertext-policy attribute-based encryption (CP-ABE) technique is used to encrypt tables published by hospitals, including patients’ Ehrs, and the primary key is stored in the database with the patient’s unique identity.
Abstract: An effectively designed e-healthcare system can significantly enhance the quality of access and experience of healthcare users, including facilitating medical and healthcare providers in ensuring a smooth delivery of services. Ensuring the security of patients' electronic health records (EHRs) in the e-healthcare system is an active research area. EHRs may be outsourced to a third-party, such as a community healthcare cloud service provider for storage due to cost-saving measures. Generally, encrypting the EHRs when they are stored in the system (i.e. data-at-rest) or prior to outsourcing the data is used to ensure data confidentiality. Searchable encryption (SE) scheme is a promising technique that can ensure the protection of private information without compromising on performance. In this paper, we propose a novel framework for controlling access to EHRs stored in semi-trusted cloud servers (e.g. a private cloud or a community cloud). To achieve fine-grained access control for EHRs, we leverage the ciphertext-policy attribute-based encryption (CP-ABE) technique to encrypt tables published by hospitals, including patients' EHRs, and the table is stored in the database with the primary key being the patient's unique identity. Our framework can enable different users with different privileges to search on different database fields. Differ from previous attempts to secure outsourcing of data, we emphasize the control of the searches of the fields within the database. We demonstrate the utility of the scheme by evaluating the scheme using datasets from the University of California, Irvine.
TL;DR: This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments which mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications.
Abstract: A smart home is a home environment enriched with sensing, actuation, communication and computation capabilities which permits to adapt it to inhabitants preferences and requirements. Establishing a proper strategy of actuation on the home environment can require complex computational tasks on the sensed data. This is the case of activity recognition, which consists in retrieving high-level knowledge about what occurs in the home environment and about the behaviour of the inhabitants. The inherent complexity of this application domain asks for tools able to properly support the design and implementation phases. This paper proposes a framework for the design and implementation of smart home applications focused on activity recognition in home environments. The framework mainly relies on the Cloud-assisted Agent-based Smart home Environment (CASE) architecture offering basic abstraction entities which easily allow to design and implement Smart Home applications. CASE is a three layered architecture which exploits the distributed multi-agent paradigm and the cloud technology for offering analytics services. Details about how to implement activity recognition onto the CASE architecture are supplied focusing on the low-level technological issues as well as the algorithms and the methodologies useful for the activity recognition. The effectiveness of the framework is shown through a case study consisting of a daily activity recognition of a person in a home environment.
TL;DR: A differential privacy protection scheme for big data in body sensor network is developed that will provide privacy protection with higher availability and reliability and the concept of dynamic noise thresholds is introduced, which makes the scheme more suitable to process big data.
Abstract: In Body Area Networks (BANs), big data collected by wearable sensors usually contain sensitive information, which is compulsory to be appropriately protected. Previous methods neglected privacy protection issue, leading to privacy exposure. In this paper, a differential privacy protection scheme for big data in body sensor network is developed. Compared with previous methods, this scheme will provide privacy protection with higher availability and reliability. We introduce the concept of dynamic noise thresholds, which makes our scheme more suitable to process big data. Experimental results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy.
TL;DR: The purpose of this review is to show the feasibility of applying intelligent agents in the healthcare domain and use the findings to provide a discussion of current trends and devise future research directions.
Abstract: Intelligent agents and healthcare have been intimately linked in the last years. The intrinsic complexity and diversity of care can be tackled with the flexibility, dynamics and reliability of multi-agent systems. The purpose of this review is to show the feasibility of applying intelligent agents in the healthcare domain and use the findings to provide a discussion of current trends and devise future research directions. A review of the most recent literature (2009---2014) of applications of agents in healthcare is discussed, and two classifications considering the main goal of the health systems as well as the main actors involved have been investigated. This review shows that the number of published works exhibits a growing interest of researchers in this field in a wide range of applications.
TL;DR: This paper proposes a new method for detection and classification of shockable ventricular arrhythmia (VT/VF) and non-shockable Ventricular arrHythmia episodes from Electrocardiogram (ECG) signal and results reveal that the feature subset derived from mutual information based scoring and the RF classifier produces accuracy, sensitivity and specificity values.
Abstract: Ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac ailments. Detection of VT/VF is one of the important step in both automated external defibrillator (AED) and implantable cardioverter defibrillator (ICD) therapy. In this paper, we propose a new method for detection and classification of shockable ventricular arrhythmia (VT/VF) and non-shockable ventricular arrhythmia (normal sinus rhythm, ventricular bigeminy, ventricular ectopic beats, and ventricular escape rhythm) episodes from Electrocardiogram (ECG) signal. The variational mode decomposition (VMD) is used to decompose the ECG signal into number of modes or sub-signals. The energy, the renyi entropy and the permutation entropy of first three modes are evaluated and these values are used as diagnostic features. The mutual information based feature scoring is employed to select optimal set of diagnostic features. The performance of the diagnostic features is evaluated using random forest (RF) classifier. Experimental results reveal that, the feature subset derived from mutual information based scoring and the RF classifier produces accuracy, sensitivity and specificity values of 97.23 %, 96.54 %, and 97.97 %, respectively. The proposed method is compared with some of the existing techniques for detection of shockable ventricular arrhythmia episodes from ECG.
TL;DR: This systematic review seeks to explore the use of telemedicine in rural Native American communities using the framework of cost, quality, and access as promulgated by the Affordable Care Act of 2010 and urge additional legislation to increase its use in this vulnerable population.
Abstract: Native American communities face serious health disparities and, living in rural areas, often lack regular access to healthcare services as compared to other Americans. Since the early 1970's, telecommunication technology has been explored as a means to address the cost and quality of, as well as access to, healthcare on rural reservations. This systematic review seeks to explore the use of telemedicine in rural Native American communities using the framework of cost, quality, and access as promulgated by the Affordable Care Act of 2010 and urge additional legislation to increase its use in this vulnerable population. As a systematic literature review, this study analyzes 15 peer-reviewed articles from four databases using the themes of cost, quality, and access. The theme of access was referenced most frequently in the reviewed literature, indicating that access to healthcare may be the biggest obstacle facing widespread adoption of telemedicine programs on rural Native American reservations. The use of telemedicine mitigates the costs of healthcare, which impede access to high-quality care delivery and, in some cases, deters prospective patients from accessing healthcare at all. Telemedicine offers rural Native American communities a means of accessing healthcare without incurring high costs. With attention to reimbursement policies, educational services, technological infrastructure, and culturally competent care, telemedicine has the potential to decrease costs, increase quality, and increase access to healthcare for rural Native American patients. While challenges facing the implementation of telemedicine programs exist, there is great potential for it to improve healthcare delivery in rural Native American communities. Public policy that increases funding for programs that help to expand access to healthcare for Native Americans will improve outcomes because of the increase in access.
TL;DR: This work proposes a new authentication scheme, which provides anonymity, unlinkability, and message authentication, and allows patients to directly and remotely consult with doctors, and is more efficient in terms of computation cost.
Abstract: Medical systems allow patients to receive care at different hospitals. However, this entails considerable inconvenience through the need to transport patients and their medical records between hospitals. The development of Telecare Medicine Information Systems (TMIS) makes it easier for patients to seek medical treatment and to store and access medical records. However, medical data stored in TMIS is not encrypted, leaving patients' private data vulnerable to external leaks. In 2014, scholars proposed a new cloud-based medical information model and authentication scheme which would not only allow patients to remotely access medical services but also protects patient privacy. However, this scheme still fails to provide patient anonymity and message authentication. Furthermore, this scheme only stores patient medical data, without allowing patients to directly access medical advice. Therefore, we propose a new authentication scheme, which provides anonymity, unlinkability, and message authentication, and allows patients to directly and remotely consult with doctors. In addition, our proposed scheme is more efficient in terms of computation cost. The proposed system was implemented in Android system to demonstrate its workability.
TL;DR: WhatsApp is useful a communication tool between physicians, especially for ED consultants who are outside the hospital, because of the ability to transfer large amounts of clinical and radiological data during a short period of time.
Abstract: The aim of this study was to evaluate WhatsApp messenger usage for communication between consulting and emergency physicians. A retrospective, observational study was conducted in the emergency department (ED) of a tertiary care university hospital between January 2014 and June 2014. A total of 614 consultations requested by using the WhatsApp application were evaluated, and 519 eligible consultations were included in the study. The WhatsApp messages that were transferred to consultant physicians consisted of 510 (98.3 %) photographic images, 517 (99.6 %) text messages, 59 (11.3 %) videos, and 10 (1.9 %) voice messages. Consultation was most frequently requested from the orthopedics clinic (n?=?160, 30.8 %). The majority of requested consultations were terminated only by evaluation via WhatsApp messages. (n?=?311, 59.9 %). Most of the consulting physicians were outside of the hospital or were mobile at the time of the consultation (n?=?292, 56.3 %). The outside consultation request rate was significantly higher for night shifts than for day shifts (p?=?.004), and the majority of outside consultation request were concluded by only WhatsApp application (p?
TL;DR: Two mHealth applications are introduced, which can be employed as the terminals of bigdata based health service to collect information for electronic medical records (EMRs) and a voice interactive serious game as a likely solution for providing assistive rehabilitation tool for therapists.
Abstract: In this paper, two mHealth applications are introduced, which can be employed as the terminals of bigdata based health service to collect information for electronic medical records (EMRs). The first one is a hybrid system for improving the user experience in the hyperbaric oxygen chamber by 3D stereoscopic virtual reality glasses and immersive perception. Several HMDs have been tested and compared. The second application is a voice interactive serious game as a likely solution for providing assistive rehabilitation tool for therapists. The recorder of the voice of patients could be analysed to evaluate the long-time rehabilitation results and further to predict the rehabilitation process.
TL;DR: It was showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data by using support vector machine.
Abstract: Acute coronary syndrome (ACS) is a serious condition arising from an imbalance of supply and demand to meet myocardium's metabolic needs. Patients typically present with retrosternal chest pain radiating to neck and left arm. Electrocardiography (ECG) and laboratory tests are used indiagnosis. However in emergency departments, there are some difficulties for physicians to decide whether hospitalizing, following up or discharging the patient. The aim of the study is to diagnose ACS and helping the physician with his decisionto discharge or to hospitalizevia machine learning techniques such as support vector machine (SVM) by using patient data including age, sex, risk factors, and cardiac enzymes (CK-MB, Troponin I) of patients presenting to emergency department with chest pain. Clinical, laboratory, and imaging data of 228 patients presenting to emergency department with chest pain were reviewedand the performance of support vector machine. Four different methods (Support vector machine (SVM), Artificial neural network (ANN), Naive Bayes and Logistic Regression) were tested and the results of SVM which has the highest accuracy is reported. Among 228 patients aged 19 to 91 years who were included in the study, 99 (43.4 %) were qualified as ACS, while 129 (56.5 %) had no ACS. The classification model using SVM attained a 99.13 % classification success. The present study showed a 99.13 % classification success for ACS diagnosis attained by Support Vector Machine. This study showed that machine learning techniques may help emergency department staff make decisions by rapidly producing relevant data.
TL;DR: Experimental results showed that the developed hybrid system entitled kmAW + SVM gave better results compared to other methods described in the literature and can be used as a useful medical decision support tool.
Abstract: The use of machine learning tools has become widespread in medical diagnosis. The main reason for this is the effective results obtained from classification and diagnosis systems developed to help medical professionals in the diagnosis phase of diseases. The primary objective of this study is to improve the accuracy of classification in medical diagnosis problems. To this end, studies were carried out on 3 different datasets. These datasets are heart disease, Parkinson's disease (PD) and BUPA liver disorders. Key feature of these datasets is that they have a linearly non-separable distribution. A new method entitled k-medoids clustering-based attribute weighting (kmAW) has been proposed as a data preprocessing method. The support vector machine (SVM) was preferred in the classification phase. In the performance evaluation stage, classification accuracy, specificity, sensitivity analysis, f-measure, kappa statistics value and ROC analysis were used. Experimental results showed that the developed hybrid system entitled kmAW?+?SVM gave better results compared to other methods described in the literature. Consequently, this hybrid intelligent system can be used as a useful medical decision support tool.
TL;DR: This paper presents a RFID mutual authentication scheme based on elliptic curve cryptography (ECC) to enhance patient medication safety and has better performance in terms of computational cost and communication overhead.
Abstract: Patient medication safety is an important issue in patient medication systems. In order to prevent medication errors, integrating Radio Frequency Identification (RFID) technology into automated patient medication systems is required in hospitals. Based on RFID technology, such systems can provide medical evidence for patients' prescriptions and medicine doses, etc. Due to the mutual authentication between the medication server and the tag, RFID authentication scheme is the best choice for automated patient medication systems. In this paper, we present a RFID mutual authentication scheme based on elliptic curve cryptography (ECC) to enhance patient medication safety. Our scheme can achieve security requirements and overcome various attacks existing in other schemes. In addition, our scheme has better performance in terms of computational cost and communication overhead. Therefore, the proposed scheme is well suitable for patient medication systems.
TL;DR: This study validated the accuracy of a cuff-less approach for ABPM using pulse arrival time (PAT) measurements on both healthy and hypertensive subjects for potential use in hypertensive management, which is the first of its kind.
Abstract: Ambulatory blood pressure monitoring (ABPM) has become an essential tool in the diagnosis and management of hypertension. Current standard ABPM devices use an oscillometric cuff-based method which can cause physical discomfort to the patients with repeated inflations and deflations, especially during nighttime leading to sleep disturbance. The ability to measure ambulatory BP accurately and comfortably without a cuff would be attractive. This study validated the accuracy of a cuff-less approach for ABPM using pulse arrival time (PAT) measurements on both healthy and hypertensive subjects for potential use in hypertensive management, which is the first of its kind. The wearable cuff-less device was evaluated against a standard cuff-based device on 24 subjects of which 15 have known hypertension. BP measurements were taken from each subject over a 24-h period by the cuff-less and cuff-based devices every 15 to 30 minutes during daily activities. Mean BP of each subject during daytime, nighttime and over 24-h were calculated. Agreement between mean nighttime systolic BP (SBP) and diastolic (DBP) measured by the two devices evaluated using Bland-Altman plot were ?1.4 ± 6.6 and 0.4 ± 6.7 mmHg, respectively. Receiver operator characteristics (ROC) statistics was used to assess the diagnostic accuracy of the cuff-less approach in the detection of BP above the hypertension threshold during nighttime (>120/70 mmHg). The area under ROC curves were 0.975/0.79 for nighttime. The results suggest that PAT-based approach is accurate and promising for ABPM without the issue of sleep disturbances associated with cuff-based devices.