Journal of Medical Systems (J Med Syst)

Publisher: Springer Verlag

Journal description

Journal of Medical Systems provides a forum for the presentation and discussion of the increasingly extensive applications of new systems techniques and methods in hospital clinic and physician's office administration; pathology radiology and pharmaceutical delivery systems; medical records storage and retrieval; and ancillary patient-support systems. The journal publishes informative articles essays and studies across the entire scale of medical systems from large hospital programs to novel small-scale medical services. Education is an integral part of this amalgamation of sciences and selected articles are published in this area. Since existing medical systems are constantly being modified to fit particular circumstances and to solve specific problems the journal includes a special section devoted to status reports on current installations.

Current impact factor: 2.21

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 1.372
2012 Impact Factor 1.783
2011 Impact Factor 1.132
2010 Impact Factor 1.064
2009 Impact Factor 0.654
2008 Impact Factor 0.674
2007 Impact Factor 0.45
2006 Impact Factor 0.581

Impact factor over time

Impact factor
Year

Additional details

5-year impact 1.86
Cited half-life 4.00
Immediacy index 0.14
Eigenfactor 0.00
Article influence 0.33
Website Journal of Medical Systems website
Other titles Journal of medical systems (Online)
ISSN 1573-689X
OCLC 44169645
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Springer Verlag

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on pre-print servers such as arXiv.org
    • Author's post-print on author's personal website immediately
    • Author's post-print on any open access repository after 12 months after publication
    • Publisher's version/PDF cannot be used
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany link to published version (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
  • Classification
    ​ green

Publications in this journal

  • Borja Gamecho · Hugo Silva · José Guerreiro · Luis Gardeazabal · Julio Abascal
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    ABSTRACT: Biofeedback from physical rehabilitation exercises has proved to lead to faster recovery, better outcomes, and increased patient motivation. In addition, it allows the physical rehabilitation processes carried out at the clinic to be complemented with exercises performed at home. However, currently existing approaches rely mostly on audio and visual reinforcement cues, usually presented to the user on a computer screen or a mobile phone interface. Some users, such as elderly people, can experience difficulties to use and understand these interfaces, leading to non-compliance with the rehabilitation exercises. To overcome this barrier, latest biosignal technologies can be used to enhance the efficacy of the biofeedback, decreasing the complexity of the user interface. In this paper we propose and validate a context-aware framework for the use of animatronic biofeedback, as a way of potentially increasing the compliance of elderly users with physical rehabilitation exercises performed at home. In the scope of our work, animatronic biofeedback entails the use of pre-programmed actions on a robot that are triggered in response to certain changes detected in the users biomechanical or electrophysiological signals. We use electromyographic and accelerometer signals, collected in real time, to monitor the performance of the user while executing the exercises, and a mobile robot to provide animatronic reinforcement cues associated with their correct or incorrect execution. A context-aware application running on a smartphone aggregates the sensor data and controls the animatronic feedback. The acceptability of the animatronic biofeedback has been tested on a set of volunteer elderly users, and results suggest that the participants found the animatronic feedback engaging and of added value.
    Journal of Medical Systems 11/2015; 39(11):296. DOI:10.1007/s10916-015-0296-1
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    ABSTRACT: Classification is the problem of identifying a set of categories where new data belong, on the basis of a set of training data whose category membership is known. Its application is wide-spread, such as the medical science domain. The issue of the classification knowledge protection has been paid attention increasingly in recent years because of the popularity of cloud environments. In the paper, we propose a Shaking Sorted-Sampling (triple-S) algorithm for protecting the classification knowledge of a dataset. The triple-S algorithm sorts the data of an original dataset according to the projection results of the principal components analysis so that the features of the adjacent data are similar. Then, we generate noise data with incorrect classes and add those data to the original dataset. In addition, we develop an effective positioning strategy, determining the added positions of noise data in the original dataset, to ensure the restoration of the original dataset after removing those noise data. The experimental results show that the disturbance effect of the triple-S algorithm on the CLC, MySVM, and LibSVM classifiers increases when the noise data ratio increases. In addition, compared with existing methods, the disturbance effect of the triple-S algorithm is more significant on MySVM and LibSVM when a certain amount of the noise data added to the original dataset is reached.
    Journal of Medical Systems 10/2015; 39(10):305. DOI:10.1007/s10916-015-0305-4
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    ABSTRACT: Wearable medical devices have become a leading trend in healthcare industry. Microcontrollers are computers on a chip with sufficient processing power and preferred embedded computing units in those devices. We have developed a software platform specifically for the design of the wearable medical applications with a small code footprint on the microcontrollers. It is supported by the open source real time operating system FreeRTOS and supplemented with a set of standard APIs for the architectural specific hardware interfaces on the microcontrollers for data acquisition and wireless communication. We modified the tick counter routine in FreeRTOS to include a real time soft clock. When combined with the multitasking features in the FreeRTOS, the platform offers the quick development of wearable applications and easy porting of the application code to different microprocessors. Test results have demonstrated that the application software developed using this platform are highly efficient in CPU usage while maintaining a small code foot print to accommodate the limited memory space in microcontrollers.
    Journal of Medical Systems 10/2015; 39(10):309. DOI:10.1007/s10916-015-0309-0
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    ABSTRACT: Medical technology makes an inevitable trend for the elderly population, therefore the intelligent home care is an important direction for science and technology development, in particular, elderly in-home safety management issues become more and more important. In this research, a low of operation algorithm and using the triangular pattern rule are proposed, then can quickly detect fall-down movements of humanoid by the installation of a robot with camera vision at home that will be able to judge the fall-down movements of in-home elderly people in real time. In this paper, it will present a preliminary design and experimental results of fall-down movements from body posture that utilizes image pre-processing and three triangular-mass-central points to extract the characteristics. The result shows that the proposed method would adopt some characteristic value and the accuracy can reach up to 90 % for a single character posture. Furthermore the accuracy can be up to 100 % when a continuous-time sampling criterion and support vector machine (SVM) classifier are used.
    Journal of Medical Systems 10/2015; 39(10):286. DOI:10.1007/s10916-015-0286-3
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    ABSTRACT: In this study, an automatic malaria parasite detector is proposed to perceive the malaria-infected erythrocytes in a blood smear image and to separate parasites from the infected erythrocytes. The detector hence can verify whether a patient is infected with malaria. It could more objectively and efficiently help a doctor in diagnosing malaria. The experimental results show that the proposed method can provide impressive performance in segmenting the malaria-infected erythrocytes and the parasites from a blood smear image taken under a microscope. This paper also presents a weighted Sobel operation to compute the image gradient. The experimental results demonstrates that the weighted Sobel operation can provide more clear-cut and thinner object contours in object segmentation.
    Journal of Medical Systems 10/2015; 39(10):280. DOI:10.1007/s10916-015-0280-9
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    ABSTRACT: One of the main challenges on Ambient Assisted Living (AAL) is to reach an appropriate acceptance level of the assistive systems, as well as to analyze and monitor end user tasks in a feasible and efficient way. The development and evaluation of AAL solutions based on user-centered perspective help to achive these goals. In this work, we have designed a methodology to integrate and develop analytics user-centered tools into assistive systems. An analysis software tool gathers information of end users from adapted psychological questionnaires and naturalistic observation of their own context. The aim is to enable an in-deep analysis focused on improving the life quality of elderly people and their caregivers.
    Journal of Medical Systems 10/2015; 39(10):291. DOI:10.1007/s10916-015-0291-6
  • Source
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    ABSTRACT: End-user development is a new trend to provide tailored services to dynamic environments such as hospitals. These services not only facilitate daily work for pharmacy personnel but also improve self-care in elder people that are still related to hospital, such as discharged patients. This paper presents an ambient intelligence (AmI) environment for End-user service provisioning in the pharmacy department of Gregorio Marañón Hospital in Madrid, composed of a drug traceability infrastructure (DP-TraIN) and a ubiquitous application for enabling the pharmacy staff to create and execute their own services for facilitating drug management and dispensing. The authors carried out a case study with various experiments where different roles from the pharmacy department of Gregorio Marañón Hospital were involved in activities such as drug identification, dispensing and medication administering. The authors analyzed the effort required to create services by pharmacy staff, the discharged patients' perception of the AmI environment and the quantifiable benefits in reducing patient waiting time for drug dispensing.
    Journal of Medical Systems 10/2015; 39(10):298. DOI:10.1007/s10916-015-0298-z
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    ABSTRACT: Indirect immunofluorescence technique applied on HEp-2 cell substrates provides the major screening method to detect ANA patterns in the diagnosis of autoimmune diseases. Currently, the ANA patterns are mostly inspected by experienced physicians to identify abnormal cell patterns. The objective of this study is to design a computer-assisted system to automatically detect cell patterns of IIF images for the diagnosis of autoimmune diseases in the clinical setting. The system simulates the functions of modern flow cytometer and provides the diagnostic reports generated by the system to the technicians and physicians through the radar graphs, box-plots, and tables. The experimental results show that, among the IIF images collected from 17 patients, 6 were classified as coarse-speckled, 3 as diffused, 2 as discrete-speckled, 1 as fine-speckled, 2 as nucleolar, and 3 as peripheral patterns, which were consistent with the patterns determined by the physicians. In addition to recognition of cell patterns, the system also provides the function to automatically generate the report for each patient. The time needed for the whole procedure is less than 30 min, which is more efficient than the manual operation of the physician after inspecting the ANA IIF images. Besides, the system can be easily deployed on many desktop and laptop computers. In conclusion, the designed system, containing functions for automatic detection of ANA cell pattern and generation of diagnostic report, is effective and efficient to assist physicians to diagnose patients with autoimmune diseases. The limitations of the current developed system include (1) only a unique cell pattern was considered for the IIF images collected from a patient, and (2) the cells during the process of mitosis were not adopted for cell classification.
    Journal of Medical Systems 10/2015; 39(10):314. DOI:10.1007/s10916-015-0314-3
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    ABSTRACT: Diabetes is considered a chronic disease that incurs various types of cost to the world. One major challenge in the control of Diabetes is the real time determination of the proper insulin dose. In this paper, we develop a prototype for real time blood sugar control, integrated with the cloud. Our system controls blood sugar by observing the blood sugar level and accordingly determining the appropriate insulin dose based on patient's historical data, all in real time and automatically. To determine the appropriate insulin dose, we propose two statistical models for modeling blood sugar profiles, namely ARIMA and Markov-based model. Our experiment used to evaluate the performance of the two models shows that the ARIMA model outperforms the Markov-based model in terms of prediction accuracy.
    Journal of Medical Systems 10/2015; 39(10):301. DOI:10.1007/s10916-015-0301-8
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    ABSTRACT: A three-dimensional (3D) model of the skull base was reconstructed from the pre- and post-dissection head CT images and embedded in a Portable Document Format (PDF) file, which can be opened by freely available software and used offline. The CT images were segmented using a specific 3D software platform for biomedical data, and the resulting 3D geometrical models of anatomical structures were used for dual purpose: to simulate the extended endoscopic endonasal transsphenoidal approaches and to perform the quantitative analysis of the procedures. The analysis consisted of bone removal quantification and the calculation of quantitative parameters (surgical freedom and exposure area) of each procedure. The results are presented in three PDF documents containing JavaScript-based functions. The 3D-PDF files include reconstructions of the nasal structures (nasal septum, vomer, middle turbinates), the bony structures of the anterior skull base and maxillofacial region and partial reconstructions of the optic nerve, the hypoglossal and vidian canals and the internal carotid arteries. Alongside the anatomical model, axial, sagittal and coronal CT images are shown. Interactive 3D presentations were created to explain the surgery and the associated quantification methods step-by-step. The resulting 3D-PDF files allow the user to interact with the model through easily available software, free of charge and in an intuitive manner. The files are available for offline use on a personal computer and no previous specialized knowledge in informatics is required. The documents can be downloaded at http://hdl.handle.net/2445/55224 .
    Journal of Medical Systems 10/2015; 39(10):282. DOI:10.1007/s10916-015-0282-7
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    ABSTRACT: The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls.
    Journal of Medical Systems 10/2015; 39(10):294. DOI:10.1007/s10916-015-0294-3
  • Vladimir Villarreal · Ramon Hervas · Jesus Fontecha · Jose Bravo
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    ABSTRACT: The development of personalized mobile monitoring applications is a complex work. Currently, the most of applications for patients monitoring through mobile devices, is not developed considering the particular characteristics of each patient, but these applications have been developed taking into account a general behavior depending on the diseases instead of the own patients. The diseases manifest different symptoms depending on the patient situation. Mary and John (hypothetic patients) have diabetes, but the same measurement of glucose for each one affects their health in a different way. This paper describes a framework that allows the development of mobile applications, personalized for each patient, in such a way that even if they have the same disease, the application will respond to the individual needs of each patient.
    Journal of Medical Systems 10/2015; 39(10):324. DOI:10.1007/s10916-015-0324-1
  • Journal of Medical Systems 10/2015; 39(10):317. DOI:10.1007/s10916-015-0317-0
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    ABSTRACT: The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.
    Journal of Medical Systems 10/2015; 39(10):306. DOI:10.1007/s10916-015-0306-3
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    ABSTRACT: The muscle fatigue can be expressed as decrease in maximal voluntary force generating capacity of the neuromuscular system as a result of peripheral changes at the level of the muscle, and also failure of the central nervous system to drive the motoneurons adequately. In this study, a muscle fatigue detection method based on frequency spectrum of electromyogram (EMG) and mechanomyogram (MMG) has been presented. The EMG and MMG data were obtained from 31 healthy, recreationally active men at the onset, and following exercise. All participants were performed a maximally exercise session in a motor-driven treadmill by using standard Bruce protocol which is the most widely used test to predict functional capacity. The method used in the present study consists of pre-processing, determination of the energy value based on wavelet packet transform, and classification phases. The results of the study demonstrated that changes in the MMG 176-234 Hz and EMG 254-313 Hz bands are critical to determine for muscle fatigue occurred following maximally exercise session. In conclusion, our study revealed that an algorithm with EMG and MMG combination based on frequency spectrum is more effective for the detection of muscle fatigue than EMG or MMG alone.
    Journal of Medical Systems 10/2015; DOI:10.1007/s10916-015-0304-5
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    ABSTRACT: Kidney renal failure means that one's kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient's history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9-94.2 %, respectively.
    Journal of Medical Systems 10/2015; 39(10):313. DOI:10.1007/s10916-015-0313-4
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    ABSTRACT: CVD (cardiovascular disease) is one of the biggest threats to human beings nowadays. An early and quantitative diagnosis of CVD is important in extending lifespan and improving people's life quality. Coronary artery stenosis can prevent CVD. To diagnose the degree of stenosis, the inner diameter of coronary artery needs to be measured. To achieve such measurement, the coronary artery is segmented by using a method that is based on morphology and the continuity between computed tomography image slices. A centerline extraction method based on mechanical simulation is proposed. This centerline extraction method can figure out a basic framework of the coronary artery by simulating pixel dots of the artery image into mass points. Such mass points have tensile forces, with which the outer pixel dots can be drawn to the center. Subsequently, the centerline of the coronary artery can be outlined by using the local line-fitting method. Finally, the nearest point method is adopted to measure the inner diameter. Experimental results showed that the methods proposed in this paper can precisely extract the centerline of the coronary artery and can accurately measure its inner diameter, thereby providing a basis for quantitative diagnosis of coronary artery stenosis.
    Journal of Medical Systems 10/2015; 39(10):329. DOI:10.1007/s10916-015-0329-9
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    ABSTRACT: In the present society, most families are double-income families, and as the long-term care is seriously short of manpower, it contributes to the rapid development of tele-homecare equipment, and the smart home care system gradually emerges, which assists the elderly or patients with chronic diseases in daily life. This study aims at interaction between persons under care and the system in various living spaces, as based on motion-sensing interaction, and the context-aware smart home care system is proposed. The system stores the required contexts in knowledge ontology, including the physiological information and environmental information of the person under care, as the database of decision. The motion-sensing device enables the person under care to interact with the system through gestures. By the inference mechanism of fuzzy theory, the system can offer advice and rapidly execute service, thus, implementing the EHA. In addition, the system is integrated with the functions of smart phone, tablet PC, and PC, in order that users can implement remote operation and share information regarding the person under care. The health care system constructed in this study enables the decision making system to probe into the health risk of each person under care; then, from the view of preventive medicine, and through a composing system and simulation experimentation, tracks the physiological trend of the person under care, and provides early warning service, thus, promoting smart home care.
    Journal of Medical Systems 09/2015; 39(9):287. DOI:10.1007/s10916-015-0287-2