Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749465
N. Cardoso, João Madureira, N. Pereira
Smartphones are everywhere, and they are a very attractive platform to perform unobtrusive monitoring of users. In this work, we use common features of modern smartphones to build a human activity recognition (HAR) system for elderly care. We have built a classifier that detects the transport mode of the user including whether an individual is inactive, walking, in bus, in car, in train or in metro. We evaluated our approach using over 24 hours of transportation data from a group of 15 individuals. Our tests show that our classifier can detect the transportation mode with over 90% accuracy.
{"title":"Smartphone-based transport mode detection for elderly care","authors":"N. Cardoso, João Madureira, N. Pereira","doi":"10.1109/HealthCom.2016.7749465","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749465","url":null,"abstract":"Smartphones are everywhere, and they are a very attractive platform to perform unobtrusive monitoring of users. In this work, we use common features of modern smartphones to build a human activity recognition (HAR) system for elderly care. We have built a classifier that detects the transport mode of the user including whether an individual is inactive, walking, in bus, in car, in train or in metro. We evaluated our approach using over 24 hours of transportation data from a group of 15 individuals. Our tests show that our classifier can detect the transportation mode with over 90% accuracy.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115746809","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}
Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749471
Hussein Alawieh, H. Hammoud, Mortada Haidar, M. Nassralla, Ahmad M. El-Hajj, Z. Dawy
This work proposes a novel patient-aware approach that utilizes an n-gram based pattern recognition algorithm to analyze scalp electroencephalogram (EEG) data and predict epileptic seizures. The method addresses the major challenge of extracting distinctive features from EEG signals through a detection of spatio-temporal signatures related to neurological events. By counting the number of occurrences of amplitude patterns with predefined lengths, the algorithm generates a probabilistic measure (anomalies ratio) that is used as a prediction marker. These extracted ratios are classified using state of the art machine learning algorithms into seizure and non-seizure windows. The efficacy of the prediction model is tested on patient records from the Freiburg database with more than 100 hours of recordings per patient and for a total of 145 seizures. The proposed algorithm is further optimized to obtain the n-gram parameters for enhanced feature extraction. Results demonstrate an average accuracy of 93.83%, sensitivity of 96.12%, and false alarm rate of 8.44%.
{"title":"Patient-aware adaptive ngram-based algorithm for epileptic seizure prediction using EEG signals","authors":"Hussein Alawieh, H. Hammoud, Mortada Haidar, M. Nassralla, Ahmad M. El-Hajj, Z. Dawy","doi":"10.1109/HealthCom.2016.7749471","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749471","url":null,"abstract":"This work proposes a novel patient-aware approach that utilizes an n-gram based pattern recognition algorithm to analyze scalp electroencephalogram (EEG) data and predict epileptic seizures. The method addresses the major challenge of extracting distinctive features from EEG signals through a detection of spatio-temporal signatures related to neurological events. By counting the number of occurrences of amplitude patterns with predefined lengths, the algorithm generates a probabilistic measure (anomalies ratio) that is used as a prediction marker. These extracted ratios are classified using state of the art machine learning algorithms into seizure and non-seizure windows. The efficacy of the prediction model is tested on patient records from the Freiburg database with more than 100 hours of recordings per patient and for a total of 145 seizures. The proposed algorithm is further optimized to obtain the n-gram parameters for enhanced feature extraction. Results demonstrate an average accuracy of 93.83%, sensitivity of 96.12%, and false alarm rate of 8.44%.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123193135","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}
Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749496
A. Khatua, Apalak Khatua
Twitter is becoming a popular social media platform for syndromic surveillance. We explored the Twitter discussion in the context of the 2016 Zika Outbreak. The Zika virus can have severe long-term (such as microcephaly in newborns) effects and not so serious immediate (such as fever or headache) effects. We explored whether social media discussions effectively capture the severity of these long-term concerns. We performed volumetric and text mining analysis, such as the co-occurrence of words and hierarchical clustering, to explore the different underlying themes in the Twitter discussion regarding the immediate and long-term concerns. Our findings suggest that the concerns related to the long-term consequences are dominant and consistent, but this is not the case for the immediate effects.
{"title":"Immediate and long-term effects of 2016 Zika Outbreak: A Twitter-based study","authors":"A. Khatua, Apalak Khatua","doi":"10.1109/HealthCom.2016.7749496","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749496","url":null,"abstract":"Twitter is becoming a popular social media platform for syndromic surveillance. We explored the Twitter discussion in the context of the 2016 Zika Outbreak. The Zika virus can have severe long-term (such as microcephaly in newborns) effects and not so serious immediate (such as fever or headache) effects. We explored whether social media discussions effectively capture the severity of these long-term concerns. We performed volumetric and text mining analysis, such as the co-occurrence of words and hierarchical clustering, to explore the different underlying themes in the Twitter discussion regarding the immediate and long-term concerns. Our findings suggest that the concerns related to the long-term consequences are dominant and consistent, but this is not the case for the immediate effects.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123736359","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}
Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749495
F. Gräßer, H. Malberg, S. Zaunseder, Stefanie Beckert, D. Küster, Jochen Schmitt, S. Abraham
In this paper two approaches for facilitating therapy decision support are proposed and compared. Both approaches, the Collaborative Recommender and hybrid Demographic-based Recommender, are based on recommender system methods which origin from the field of product recommendation in e-commerce applications. An exemplary dataset comprising health record excerpts of patients suffering from the skin disease psoriasis is used for evaluating both approaches. The approaches estimate the outcome of a subset of systemic therapies to support the medical practitioner in making therapy decisions for a specific patient and time, i.e. consultation under consideration. Both systems proved to work and are capable of assisting medical practitioners prospectively with making appropriate therapy decisions.
{"title":"Application of recommender system methods for therapy decision support","authors":"F. Gräßer, H. Malberg, S. Zaunseder, Stefanie Beckert, D. Küster, Jochen Schmitt, S. Abraham","doi":"10.1109/HealthCom.2016.7749495","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749495","url":null,"abstract":"In this paper two approaches for facilitating therapy decision support are proposed and compared. Both approaches, the Collaborative Recommender and hybrid Demographic-based Recommender, are based on recommender system methods which origin from the field of product recommendation in e-commerce applications. An exemplary dataset comprising health record excerpts of patients suffering from the skin disease psoriasis is used for evaluating both approaches. The approaches estimate the outcome of a subset of systemic therapies to support the medical practitioner in making therapy decisions for a specific patient and time, i.e. consultation under consideration. Both systems proved to work and are capable of assisting medical practitioners prospectively with making appropriate therapy decisions.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"33 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113980491","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}
Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749421
Erik Schreiber, S. Franke, R. Bieck, T. Neumuth
With each newly developed surgical assistance functionality, more information is available but also required in the operating room (OR). To prevent an information overload and support the surgical workflow, we propose a concept for a prioritized and consistent presentation of surgical information. We identify necessary tasks and requirements to present the right information, to the right time, at the right place and in the right form. For this, rule-based methods to prioritize information in respect to its relevance to the current OR situation and to orchestrate information consistently on a set of OR monitors are designed. Subsequently advantages and disadvantages of the proposed approach are discussed and next required steps for an implementation elucidated.
{"title":"A concept for consistent and prioritized presentation of surgical information","authors":"Erik Schreiber, S. Franke, R. Bieck, T. Neumuth","doi":"10.1109/HealthCom.2016.7749421","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749421","url":null,"abstract":"With each newly developed surgical assistance functionality, more information is available but also required in the operating room (OR). To prevent an information overload and support the surgical workflow, we propose a concept for a prioritized and consistent presentation of surgical information. We identify necessary tasks and requirements to present the right information, to the right time, at the right place and in the right form. For this, rule-based methods to prioritize information in respect to its relevance to the current OR situation and to orchestrate information consistently on a set of OR monitors are designed. Subsequently advantages and disadvantages of the proposed approach are discussed and next required steps for an implementation elucidated.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131142438","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}
Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749532
Stefanus A. Wirdatmadja, S. Balasubramaniam, Y. Koucheryavy, J. Jornet
In recent years, numerous research efforts have been dedicated towards developing efficient implantable devices for Deep Brain Stimulation (DBS). However, there are limitations and challenges with the current technologies. Firstly, the stimulation of neurons currently is only possible through implantable electrodes which target a population of neurons. This results in challenges in the event that stimulation at the single neuron level is required. Secondly, a major hurdle still lies in developing miniature devices that can last for a lifetime in the patient's brain. Recently, the concept of neural dust has been introduced as a way to achieve single neuron monitoring and potentially actuation. In parallel to this, the field of optogenetics has emerged where the aim is to stimulate neurons using light, usually by means of optical fibers inserted through the skull. Obviously, this introduces many challenges in terms of user friendliness and biocompatibility. We address this shortcoming by proposing the wireless optogenetic neural dust (wi-opt neural dust). The wiopt neural dust is equipped with a miniature LED that is able to stimulate the genetically engineered neurons, and at the same time harvest energy from ultrasonic vibrations. The simulation results presented in the paper investigates the behaviour of the light propagation in the brain tissue, as well as the performance of designed circuitry for the energy harvesting process. The results demonstrates the feasibility of utilizing wi-opt neural dust for long term implantation in the brain, and a new direction towards precise stimulation of neurons in the cortex.
{"title":"Wireless optogenetic neural dust for deep brain stimulation","authors":"Stefanus A. Wirdatmadja, S. Balasubramaniam, Y. Koucheryavy, J. Jornet","doi":"10.1109/HealthCom.2016.7749532","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749532","url":null,"abstract":"In recent years, numerous research efforts have been dedicated towards developing efficient implantable devices for Deep Brain Stimulation (DBS). However, there are limitations and challenges with the current technologies. Firstly, the stimulation of neurons currently is only possible through implantable electrodes which target a population of neurons. This results in challenges in the event that stimulation at the single neuron level is required. Secondly, a major hurdle still lies in developing miniature devices that can last for a lifetime in the patient's brain. Recently, the concept of neural dust has been introduced as a way to achieve single neuron monitoring and potentially actuation. In parallel to this, the field of optogenetics has emerged where the aim is to stimulate neurons using light, usually by means of optical fibers inserted through the skull. Obviously, this introduces many challenges in terms of user friendliness and biocompatibility. We address this shortcoming by proposing the wireless optogenetic neural dust (wi-opt neural dust). The wiopt neural dust is equipped with a miniature LED that is able to stimulate the genetically engineered neurons, and at the same time harvest energy from ultrasonic vibrations. The simulation results presented in the paper investigates the behaviour of the light propagation in the brain tissue, as well as the performance of designed circuitry for the energy harvesting process. The results demonstrates the feasibility of utilizing wi-opt neural dust for long term implantation in the brain, and a new direction towards precise stimulation of neurons in the cortex.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128704262","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}
Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749473
P. Rajković, D. Jankovic, A. Milenković
Developing suggestion tools in the scope of health information systems can be a complex task, followed by a risk of not being accepted by the end users. Thus, we decide to start the implementation around the existing functionality. In this paper we present a case study showing the adaptation of e-learning medical information system extension to a set of simple suggestion tools. While some features of initial system had to be modified, the domain specific knowledge collected for the e-learning extension is used to suppress potential errors. Presented suggestion tool is based on highly configurable lists of pre-defined entities that can be easily selected, and after the verification from the medical practitioner, copied into an active visit. After four years of active use, and several iteration of update, described suggestion tools are mostly accepted among the general practitioners, especially within certain scenarios where faster medication prescription is a must.
{"title":"Adaption of medical information system's e-learning extension to a simple suggestion tool","authors":"P. Rajković, D. Jankovic, A. Milenković","doi":"10.1109/HealthCom.2016.7749473","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749473","url":null,"abstract":"Developing suggestion tools in the scope of health information systems can be a complex task, followed by a risk of not being accepted by the end users. Thus, we decide to start the implementation around the existing functionality. In this paper we present a case study showing the adaptation of e-learning medical information system extension to a set of simple suggestion tools. While some features of initial system had to be modified, the domain specific knowledge collected for the e-learning extension is used to suppress potential errors. Presented suggestion tool is based on highly configurable lists of pre-defined entities that can be easily selected, and after the verification from the medical practitioner, copied into an active visit. After four years of active use, and several iteration of update, described suggestion tools are mostly accepted among the general practitioners, especially within certain scenarios where faster medication prescription is a must.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122314819","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}
Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749481
K. J. Francis, P. Mishra, P. Rajalakshmi, Sumohana S. Channappayya, A. Richhariya
Accurate model-based methods in Photo-Acoustic Tomography (PAT) can reconstruct the image from insufficient and inaccurate measurements. Most of the models either make the simplified assumption of spherical averaging or use accurate models that have computationally burdensome implementations. We present a simple and accurate measurement matrix that is derived from the pseudo-spectral PAT model. The accuracy of the measurement matrix is first validated against the experimental PAT signal. We also compare the model against the standard k-wave measurement model and the spherical averaging model. We then highlight several reconstruction strategies based on the nature of the region of interest to further demonstrate the accuracy of the proposed measurement matrix.
{"title":"A simple and accurate matrix for model based photoacoustic imaging","authors":"K. J. Francis, P. Mishra, P. Rajalakshmi, Sumohana S. Channappayya, A. Richhariya","doi":"10.1109/HealthCom.2016.7749481","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749481","url":null,"abstract":"Accurate model-based methods in Photo-Acoustic Tomography (PAT) can reconstruct the image from insufficient and inaccurate measurements. Most of the models either make the simplified assumption of spherical averaging or use accurate models that have computationally burdensome implementations. We present a simple and accurate measurement matrix that is derived from the pseudo-spectral PAT model. The accuracy of the measurement matrix is first validated against the experimental PAT signal. We also compare the model against the standard k-wave measurement model and the spherical averaging model. We then highlight several reconstruction strategies based on the nature of the region of interest to further demonstrate the accuracy of the proposed measurement matrix.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127254655","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}
Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749444
P. Pierleoni, Luca Pernini, Lorenzo Palma, Alberto Belli, Simone Valenti, Lorenzo Maurizi, Loris Sabbatini, A. Marroni
Solutions and services for e-Health and telemedicine are constantly spreading and becoming increasingly important in the health area thanks to last innovations in electronics, informatics and telecommunications. This work proposes an innovative service for the e-Health oriented to the maximum ease of use and to the sharing of vital signs. The proposal consists in a tele-counseling service based on the WebRTC technology that allows any person residing remotely from medical staff or hospital to directly interact with them. Our solution provides all the common functions of the WebRTC, such as real-time video and audio streams, instant messaging, and file sharing with the only requirement of a traditional web browser. Beyond that, we implemented the real-time transmission and visualization of vital signs and parameters acquired by biomedical sensors connected to the patient's personal device through the RTCDataChannel. Currently our solution involves the installation of a browser extension, but this operation is very simple and may be avoided when WebRTC APIs and browsers will support media streams coming from sensors at the same way as audio and video streams. Our solution demonstrates how web technologies can be applied in the health sector providing very effective services to patients and users which for various reasons have difficulties to travel to hospitals in order to have medical support.
{"title":"An innovative WebRTC solution for e-Health services","authors":"P. Pierleoni, Luca Pernini, Lorenzo Palma, Alberto Belli, Simone Valenti, Lorenzo Maurizi, Loris Sabbatini, A. Marroni","doi":"10.1109/HealthCom.2016.7749444","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749444","url":null,"abstract":"Solutions and services for e-Health and telemedicine are constantly spreading and becoming increasingly important in the health area thanks to last innovations in electronics, informatics and telecommunications. This work proposes an innovative service for the e-Health oriented to the maximum ease of use and to the sharing of vital signs. The proposal consists in a tele-counseling service based on the WebRTC technology that allows any person residing remotely from medical staff or hospital to directly interact with them. Our solution provides all the common functions of the WebRTC, such as real-time video and audio streams, instant messaging, and file sharing with the only requirement of a traditional web browser. Beyond that, we implemented the real-time transmission and visualization of vital signs and parameters acquired by biomedical sensors connected to the patient's personal device through the RTCDataChannel. Currently our solution involves the installation of a browser extension, but this operation is very simple and may be avoided when WebRTC APIs and browsers will support media streams coming from sensors at the same way as audio and video streams. Our solution demonstrates how web technologies can be applied in the health sector providing very effective services to patients and users which for various reasons have difficulties to travel to hospitals in order to have medical support.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126799422","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}
Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749460
Christoph Thuemmler, A. Paulin, Ai Keow Lim
This paper summarizes the determinants for future e-Health network and IT infrastructures in the health care environment. The paper bases on observations conducted as part of a larger study at a university hospital in Munich, and summarizes ongoing discussions, key determinants from relevant whire papers, and challenges of the domain. The objective of the paper is to provide a broad overview over the implications of the e-Health domain to provide inputs in ongoing discussion on 5G characteristics.
{"title":"Determinants of next generation e-Health network and architecture specifications","authors":"Christoph Thuemmler, A. Paulin, Ai Keow Lim","doi":"10.1109/HealthCom.2016.7749460","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749460","url":null,"abstract":"This paper summarizes the determinants for future e-Health network and IT infrastructures in the health care environment. The paper bases on observations conducted as part of a larger study at a university hospital in Munich, and summarizes ongoing discussions, key determinants from relevant whire papers, and challenges of the domain. The objective of the paper is to provide a broad overview over the implications of the e-Health domain to provide inputs in ongoing discussion on 5G characteristics.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127320883","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}
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