2017 IEEE 30th Neumann Colloquium (NC)最新文献

{"title":"Blood glucose level prediction with improved parameter identification methods","authors":"Péter Gyuk, Istán Vassányi, I. Kósa","doi":"10.1109/NC.2017.8263257","DOIUrl":"https://doi.org/10.1109/NC.2017.8263257","url":null,"abstract":"Calculating the insulin need is an everyday task for diabetics. According to the surveys found in the literature, these estimations are sometimes inefficient in practice leading to critically high or low Blood Glucose Levels. Our goal is to make the life of these people easier by calculating their insulin need using a computer prediction model combined with a user-friendly interface. This paper proposes a prediction algorithm with parameter identification and also presents the validation of the model in outpatient care. The model itself is extended with personalized parameter training featuring Brute Force Method and Genetic Algorithm using different training parameter set sizes. For the validation, we used a data set including more than 20 diabetic patients' log files. The results showed around 55% improvement in the results with our best model training method compared to the tests performed without any parameter identification. This means that 88.5% of the predicted Blood Glucose Level values would result in a clinically acceptable decision. This is a promising result compared to others found in the literature, but there is still room for future research and improvement.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123147615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authorization solution for full stack FHIR HAPI access","authors":"Zoltán Richárd Jánki, Zoltán Szabó, Vilmos Bilicki, M. Fidrich","doi":"10.1109/NC.2017.8263266","DOIUrl":"https://doi.org/10.1109/NC.2017.8263266","url":null,"abstract":"The Health Level Seven's (HL7) Fast Healthcare Interoperability Resources (FHIR) standard enables the standardized access to the health related data stored in different Electronic Health Record (EHR) backends. A popular open source implementation of FHIR is the Java-based HAPI. It provides generic FHIR-compatible Representational State Transfer (REST) interfaces for data access. One weak point of this solution is the lack of client side support. It supports only Java-based client environments. This is useful in the case of Android-based native client, but for the JavaScript or TypeScript-based full stack environments a JavaScript-based environment is needed. Here, we present our solution which integrates HAPI into a JavaScript-based full stack environment. Another novelty of our solution is that it extends the FHIR's access control model so that it has the best aspects of Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) access control approaches.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128046575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic background-foreground segmentation of organs on MRI images of the head-neck area","authors":"László G. Varga, Viktor Szpisják","doi":"10.1109/NC.2017.8263253","DOIUrl":"https://doi.org/10.1109/NC.2017.8263253","url":null,"abstract":"Accurate segmentation of organs on MRI sequences can be crucial in patient treatment. It supports the diagnosis of diseases and helps in treatment planning. For example, in oncology, analyzing the shape of the organs can give valuable information on the extent of the tumors, and in radiation therapy planning, the delineation of organs of risk is an indispensable first step of the treatment. In this paper, we give a probability atlas, and deep convolutional network-based method for the automatic segmentation of six organs (trachea, spinal cord, parotid glands, sternocleidomastoid muscle, arteria carotis commulus and vena jugularis interna) on multi-sequential MRI images of the head-neck area. The method was also evaluated on clinical data and found to give accurate results according to the Dice metric.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"25 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130891426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biomedical applications of time series analysis","authors":"Tamás Ferenti","doi":"10.1109/NC.2017.8263256","DOIUrl":"https://doi.org/10.1109/NC.2017.8263256","url":null,"abstract":"Many biomedical data are available as time series, especially in the field of public health and epidemiology, where indicators are usually collected over time. Clinical studies with long follow-up are also sometimes best analyzed with time series methods. The analysis of administrative health care data often gives rise to time series problems too, as events are frequently converted to counts over a given interval. Finally, some biomedical measurements also may be viewed as time series, such as ECG recordings. The methods of time series analysis can be very broadly divided into two categories: time-domain and frequency-domain methods. Frequency-domain methods are based on converting the time series, classically using Fourier transform, to a form where the time series is represented as the weighted sum of sinusoids [1]. This so-called spectral analysis allows us to get insight into the periodic components of the time series, making it possible to investigate cyclicity/seasonality of the original data. Fourier transform, however, does not allow the spectrum to evolve over time, so methods were developed which make a trade-off between time resolution and frequency resolution, such as wavelet analysis [2]. In addition to the investigation of periodicity in epidemiologic data (e.g. [3]), these methods are also widely used in biomedical signal analysis, such as the analysis of ECG recordings [4]. The vast majority of time series analyses, however, apply time-domain methods. Roughly speaking, they can be divided into “classic” time series regression methods employing only exogenous regressors (which may include long-term secular trend and seasonality in epidemiology, patient characteristics in a clinical study, or the past or contemporary value of another time series that is possibly related to the one under investigation, this can include an abrupt change giving rise to segmented regression models) and methods with stochastic component (autoregressive and moving average models, and their combinations). In epidemiology, regression models are often complicated by the fact that the response variable is count data, giving rise to generalized linear models, the presence of overdispersion [5], and non-linearities [6]. These methods are often used today, from environmental epidemiology [7] to infectious diseases modelling [8]. One profound problem in time series modelling is the presence of autocorrelation. To capture the dynamics of the time series, ARIMA-models (Box-Jenkins approach) are often used in other areas; this started to appear in medicine too [9]. Other, biomedically less often used applications of time series methods include filtering/smoothing, the analysis of multivariate time series (such as VAR-models), and more complex state space models.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129851810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG processing by rational systems","authors":"Gergo Bognár, S. Fridli, P. Kovács, F. Schipp","doi":"10.1109/NC.2017.8263265","DOIUrl":"https://doi.org/10.1109/NC.2017.8263265","url":null,"abstract":"One of the most effective methods in ECG processing is the so-called transformation technique. The most popular systems that are used frequently include the classical trigonometric Fourier, orthogonal polynomial, and spline systems. In recent applications the wavelet methods turned to be especially effective. Yet we propose another system that is built from basic rational functions. The rational orthogonal systems, i.e. the Malmquist-Takenaka systems, we use are determined by parameters called poles and multiplicities. This way we have infinitely many systems at hand. The main advantage of our approach is its flexibility. The parameters that control the system can be adjusted to the particular problem. It means that in ECG processing we may change them even from heartbeat to heartbeat. We have worked out the proper discretization of the original mathematical model [2-3] and designed optimization processes adapted to these systems [4]. The result is a special variable projection method. The algorithms we constructed turned to be very efficient. They outperform the existing ones in many respects [7]. Problems we considered so far include QRS modeling [4], R peak detection, ECG compression, heartbeat classification [1]. The tests were evaluated on the standard public MIT-BIH Arrhythmia Database available on PhysioNet [5]. We note that our algorithms are fast enough for real time applications, and also for big databases. one may use them as a tool for supporting cardiologists among others as a preprocessing long time records (Holter) or as a built in alert function. So far we have concentrated on ECG processing methods that work well in general circumstances. For instance in case of classification we constructed a mixed feature vector that consists of dynamic and morphological features. The morphological features, which can be divided into patient specific and heartbeat specific components, come from rational transforms. Then we used a so called Support Vector Machine classifier for them. The method can be performed for multiple leads independently or even together, and then we can combine the individual results to obtain an improved final result. Recently, we have been focusing on more specific situations where we take various aspects into consideration. In particular we have been working on a version designed for subject-based classification Moreover we have been refining the classification algorithm in order to reduce the rate of false negative results. In connection with it various evaluation and fusion techniques have been investigated. We note that our method can be efficient for medical signals other than ECG. An example for epileptic seizure detection in EEG signals can be found in [6].","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129948827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surgical data science, an emerging field of medicine","authors":"D. A. Nagy, I. Rudas, T. Haidegger","doi":"10.1109/NC.2017.8263251","DOIUrl":"https://doi.org/10.1109/NC.2017.8263251","url":null,"abstract":"Computer Assisted Surgery (CAS) significantly changed the course of interventional medicine. The development of medical imaging opened up the possibility for accurate, patient specific planning, and advanced imaging techniques provided the ground for the development of real-time navigation systems. The advancement of minimally invasive surgical techniques and tools required increasing manuality from the surgeon, which facilitated the development of tele-robotic manipulation. These systems provide a vast amount of objective inta-operative data, thus many believe that the next step could be big data analysis for creating and evaluating surgical process models. This emerging field of medicine, called Surgical Data Science, has the potential to improve intervetional medicine with objective statistical analysis, and therefore to provide better patient outcomes and a reduction in healthcare costs.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117021643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an expert system framework for lifestyle improvement","authors":"T. Lőrincz, Benedek Szakonyi, Ágnes Lipovits, I. Vassányi","doi":"10.1109/NC.2017.8263259","DOIUrl":"https://doi.org/10.1109/NC.2017.8263259","url":null,"abstract":"In modern societies new, lifestyle related chronic diseases are appearing, affecting more and more people. Besides decreasing the quality of life for these patients, their treatments require increasing financial and social support from governments (and in many cases, even from the patients themselves). Apart from socio-economic concerns, another serious problem is the increasing shortage of experts (e.g. doctors, dietitians, ergonomists) that could help people, as the need for them is growing faster than their numbers. In this paper, a framework for creating expert systems capable of containing and properly using the knowledge of such experts, for providing help to users in acquiring and maintaining a healthy lifestyle, is presented. By selecting two different areas, diet and physical oriented lifestyle management and workplace related ergonomics, the effectiveness of such systems is tested.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129740332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The modelling and simulation of the pension system","authors":"Z. Szabó","doi":"10.1109/NC.2017.8263277","DOIUrl":"https://doi.org/10.1109/NC.2017.8263277","url":null,"abstract":"The reform of the pension system is a cardinal and noteworthy subject in all countries of the European Union and it is often discussed in various scientific meetings. These economic and social challenges necessitate long-term government strategies, which need to be modelled, simulated (tested and verified). The following exploration describes the present pillars of the Hungarian pension system and goes into detail about the problems of the mandatory social insurance system, using demographic and statistical data. The main objective of this essay is to present a possible scenario of changes in the pension benefits in Hungary. The study is based on statistical projections but also includes the results of a questionnaire-based behavioural research project, and a presentation of predicted pension expenses and pension levels in Hungary. The study consists of three parts. The first part summarizes the theoretical planning process of modelling. The second part presents the pillars of the Hungarian pension system and the achievements of pension modelling. The third part summarizes the theoretical basics and results of the research project “The impact assessment of the pension system”.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130813019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How new terciary cardiac centers influence care provider network","authors":"Zsolt Vassy, I. Vassányi, I. Kósa","doi":"10.1109/NC.2017.8263262","DOIUrl":"https://doi.org/10.1109/NC.2017.8263262","url":null,"abstract":"Stress response is a particularly good model of the reorganization of networks. Stress effect induces a significant decrease in the overlaps and connections of network modules. We have built a health care provider network based on patient's clinical pathways. We have analyzed changes in network dynamics using a 180 day wide moving window analysis moving by 30 day wide steps. We observed that after appearing of new tertiary care service both average node degree and average module number are increased. But after an adaptation time has elapsed both of network characteristic numbers are decreased. Conclusion that network stress situation did not mean that a new tertiary care provider appeared but when the old one was disconnected from the examined subnetwork.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134182976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knee-joint tissue recognition in magnetic resonance imaging","authors":"Artjoms Suponenkovs, Z. Markovičs, A. Platkājis","doi":"10.1109/NC.2017.8263280","DOIUrl":"https://doi.org/10.1109/NC.2017.8263280","url":null,"abstract":"The automatic knee-joint soft tissue recognition problem is very relevant due to increasing number of people with knee-joint diseases. It is for this reason that this paper investigates the problem of soft tissue recognition in magnetic resonance imaging (MRI). MRI is useful for knee-joint soft tissue presentation, but usually a doctor cannot see all necessary information in MRI data. Computer MRI analysis makes it possible to process all MRI data and shows additional information for the doctor. This additional information can make it easier to detect invisible injuries of knee-joint soft tissues. Knee-joint soft tissue recognition and analysis are very helpful, especially for osteoarthritis (OA) early diagnostics. Computer OA diagnostics are impossible without segmentation of knee-joint tissues. This publication describes approaches for knee-joint image pre-processing, knee-joint image segmentation, tissue recognition and tissue analysis. To solve tissue analysis task it is important to use biological information of knee-joint structure, physical and biochemical tissue features. Tissue analysis is very useful especially for early diagnostics. It allows starting treatment earlier and therefore reducing the risk of tissue destruction. It is for this reason that this paper investigates the above-mentioned challenges.","PeriodicalId":140536,"journal":{"name":"2017 IEEE 30th Neumann Colloquium (NC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116090188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}