Pub Date : 2016-09-01DOI: 10.1109/HealthCom.2016.7749443
Rahul Krishnan Pathinarupothi, M. Ramesh, E. Rangan
Remote health monitoring and delivery through mobile devices and wireless networks offers unique challenges related to performance, reliability, data size, power management, and analytical complexity. We present a multi-layered architecture that matches communication performance to medical importance of data being monitored. The priority of vital data and the context of sensing are used to select the communication medium and the power management policies. Further smartness is introduced into data summarization by employing a severity level quantizer, followed by a consensus abnormality motif discovery and an alert mechanism that prioritizes doctors' consultative time. We also present our successful implementation of the above multi-layered architecture in a system developed to remotely monitor cardiac patients.
{"title":"Multi-layer architectures for remote health monitoring","authors":"Rahul Krishnan Pathinarupothi, M. Ramesh, E. Rangan","doi":"10.1109/HealthCom.2016.7749443","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749443","url":null,"abstract":"Remote health monitoring and delivery through mobile devices and wireless networks offers unique challenges related to performance, reliability, data size, power management, and analytical complexity. We present a multi-layered architecture that matches communication performance to medical importance of data being monitored. The priority of vital data and the context of sensing are used to select the communication medium and the power management policies. Further smartness is introduced into data summarization by employing a severity level quantizer, followed by a consensus abnormality motif discovery and an alert mechanism that prioritizes doctors' consultative time. We also present our successful implementation of the above multi-layered architecture in a system developed to remotely monitor cardiac patients.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"157 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":"128100276","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.2016.7749434
Jolanta Mizera-Pietraszko
eHealth services integrate Web Information Retrieval and Intelligent Medical Decision Support for health care professionals based on the range of possible symptoms which a patient reports. However, many symptoms like high temperature, fever, or headache, are ambiguous in terms of suggesting wide variety of possible patient's conditions to the GP, while other symptoms are mutually dependant, which again can be misleading to make an accurate diagnosis. On the other hand, doctor's up-to-date knowledge on the medicaments, drugs, active medical substances included, anticipated range of diseases relating to the symptoms reported, and the most reliable pharmaceutical manufacturers, are of the greatest importance to cure the illness successfully. This study proposes an approach to support so called standard medical procedure or clinical guidelines in treatment of each of the diseases by delivering such a knowledge to the physician and by individualizing the selection of drugs in respect to the patient's specific needs in order to avoid a potential drug interaction. We evaluate efficiency of a medical multilingual decision support system Diagnosia on the grounds of accessibility to such a knowledge depending on the EU language and we use Bayesian inference for generating the optimal decision on reaching a particular diagnosis accuracy. Our methodology is examined on the real data taken from the American service Prescriber Checkup and some other knowledge-based medical resources of nationally recognized rank. Our findings indicate that this approach outperforms not only traditional standard procedure of curing some commonly occurring illnesses, but also many commercial computer-assisted medical support diagnostic systems.
{"title":"Computer-assisted clinical diagnosis in the official European union languages","authors":"Jolanta Mizera-Pietraszko","doi":"10.1109/HealthCom.2016.7749434","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749434","url":null,"abstract":"eHealth services integrate Web Information Retrieval and Intelligent Medical Decision Support for health care professionals based on the range of possible symptoms which a patient reports. However, many symptoms like high temperature, fever, or headache, are ambiguous in terms of suggesting wide variety of possible patient's conditions to the GP, while other symptoms are mutually dependant, which again can be misleading to make an accurate diagnosis. On the other hand, doctor's up-to-date knowledge on the medicaments, drugs, active medical substances included, anticipated range of diseases relating to the symptoms reported, and the most reliable pharmaceutical manufacturers, are of the greatest importance to cure the illness successfully. This study proposes an approach to support so called standard medical procedure or clinical guidelines in treatment of each of the diseases by delivering such a knowledge to the physician and by individualizing the selection of drugs in respect to the patient's specific needs in order to avoid a potential drug interaction. We evaluate efficiency of a medical multilingual decision support system Diagnosia on the grounds of accessibility to such a knowledge depending on the EU language and we use Bayesian inference for generating the optimal decision on reaching a particular diagnosis accuracy. Our methodology is examined on the real data taken from the American service Prescriber Checkup and some other knowledge-based medical resources of nationally recognized rank. Our findings indicate that this approach outperforms not only traditional standard procedure of curing some commonly occurring illnesses, but also many commercial computer-assisted medical support diagnostic systems.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"2 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":"132907724","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.2016.7749463
M. Raatikainen, Robert Ciszek, J. Närväinen, J. Merilahti, S. Siikanen, Timo Ollikainen, Ilona Hallikainen, Jukka-Pekka Skon
A customized intelligent lighting control combined with an indoor environment monitoring system is presented as a novel system architecture for the help of elderly, especially for people with dementia. Bluish light, which affects human circadian rhythm, is the key element of this study aiming to find ways to enhance patient wellbeing and reduce nursing workload. Moreover, thermal comfort of occupants is monitored and discussed.
{"title":"System architecture of customized intelligent lighting control and indoor environment monitoring system for persons with mild cognitive impairment or dementia","authors":"M. Raatikainen, Robert Ciszek, J. Närväinen, J. Merilahti, S. Siikanen, Timo Ollikainen, Ilona Hallikainen, Jukka-Pekka Skon","doi":"10.1109/HealthCom.2016.7749463","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749463","url":null,"abstract":"A customized intelligent lighting control combined with an indoor environment monitoring system is presented as a novel system architecture for the help of elderly, especially for people with dementia. Bluish light, which affects human circadian rhythm, is the key element of this study aiming to find ways to enhance patient wellbeing and reduce nursing workload. Moreover, thermal comfort of occupants is monitored and discussed.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"36 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":"123540430","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.2016.7749476
Christian Lins, A. Hein, L. Halder, Philipp Gronotte
In this paper, results from the long-term usage of a mobile application (app) for seniors that encourages physical and mental activity are presented. The application was designed for elderly inhabitants of senior residences to motivate them to increase their physical and mental activity in everyday life. Usage statistics of 82 users for about two years were processed and show that the active elderly users can be clustered in two groups with either increasing or decreasing and very little constant activity. Users with decreasing activity have also shown decreasing usage errors with the app's user interface which may indicate that they are growing out of the app. The results show insight view about the usage and suggest that the Concept of Flow can be applied here.
{"title":"Still in flow — long-term usage of an activity motivating app for seniors","authors":"Christian Lins, A. Hein, L. Halder, Philipp Gronotte","doi":"10.1109/HealthCom.2016.7749476","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749476","url":null,"abstract":"In this paper, results from the long-term usage of a mobile application (app) for seniors that encourages physical and mental activity are presented. The application was designed for elderly inhabitants of senior residences to motivate them to increase their physical and mental activity in everyday life. Usage statistics of 82 users for about two years were processed and show that the active elderly users can be clustered in two groups with either increasing or decreasing and very little constant activity. Users with decreasing activity have also shown decreasing usage errors with the app's user interface which may indicate that they are growing out of the app. The results show insight view about the usage and suggest that the Concept of Flow can be applied here.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"5 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":"121810930","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.2016.7749475
Mário W. L. Moreira, J. Rodrigues, Antonio M. B. Oliveira, K. Saleem, Augusto J. V. Neto
Significant advances on smart decision support systems (DSSs) development have influenced important results on pregnancy care. Nevertheless, even considering the efforts to reduce the number of women deaths due to problems related to pregnancy, this decrease presented less impact than other areas of human development. Hypertensive disorders in pregnancy, particularly pre-eclampsia and eclampsia, account for significant proportion of perinatal morbidity and maternal mortality. In this context, this paper proposes an inference model that uses data mining (DM) techniques capable for operating in a data set to extract patterns and assist in knowledge discovery. Identifying hypertensive crises that complicate pregnancy, it can impact in a meaningful reduction the incidence of sequelae and death of pregnant women. Comparison between two Bayesian classifiers is performed in this work to better classify the hypertensive disorders severity. Results showed that Naïve Bayes classifier had an excellent performance, presenting better precision and F-measure, compared to the other experimented classifiers. Even finding a good performance to predict hypertensive disorders, other Bayesian methods need to be evaluated, as well as other DM techniques such as those based on artificial intelligence (AI) and tree-based methods.
{"title":"An inference mechanism using Bayes-based classifiers in pregnancy care","authors":"Mário W. L. Moreira, J. Rodrigues, Antonio M. B. Oliveira, K. Saleem, Augusto J. V. Neto","doi":"10.1109/HealthCom.2016.7749475","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749475","url":null,"abstract":"Significant advances on smart decision support systems (DSSs) development have influenced important results on pregnancy care. Nevertheless, even considering the efforts to reduce the number of women deaths due to problems related to pregnancy, this decrease presented less impact than other areas of human development. Hypertensive disorders in pregnancy, particularly pre-eclampsia and eclampsia, account for significant proportion of perinatal morbidity and maternal mortality. In this context, this paper proposes an inference model that uses data mining (DM) techniques capable for operating in a data set to extract patterns and assist in knowledge discovery. Identifying hypertensive crises that complicate pregnancy, it can impact in a meaningful reduction the incidence of sequelae and death of pregnant women. Comparison between two Bayesian classifiers is performed in this work to better classify the hypertensive disorders severity. Results showed that Naïve Bayes classifier had an excellent performance, presenting better precision and F-measure, compared to the other experimented classifiers. Even finding a good performance to predict hypertensive disorders, other Bayesian methods need to be evaluated, as well as other DM techniques such as those based on artificial intelligence (AI) and tree-based methods.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"64 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":"121813632","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.2016.7749511
Daniela Antkowiak, Christian Kohlschein, Roksaneh Krooß, Maximilian Speicher, Tobias Meisen, S. Jeschke, C. Werner
In Europe there are more than 580 000 people who suffer from aphasia - an acquired speech and language disorder that occurs because of brain damage, primarily as a result of a stroke. Especially with regards to demographic change, health care systems have to face present and future challenges to improve aphasia therapy. Thereby, immediate therapeutic measures are decisive for best possible and long-term success in language therapy. Regarding essential requirements, on the one hand, therapy intensity and frequency have to be increased significantly while on the other hand, measures need to be adjusted along everyday activities. A very promising approach to meet this requirements are augmented reality applications. They can be used to create a highly natural exercise situation, in which patients interact and practice with their personal possessions at home. This facilitates the successful and continuous transfer of learnings for the patient, contrary to being solely dependent on clinical therapy units. This paper gives an overview of the concept of a real-time software providing augmented and dynamic language therapy, which is interactive and utilizes simple user interface design, for home-based training.
{"title":"Language therapy of aphasia supported by augmented reality applications","authors":"Daniela Antkowiak, Christian Kohlschein, Roksaneh Krooß, Maximilian Speicher, Tobias Meisen, S. Jeschke, C. Werner","doi":"10.1109/HealthCom.2016.7749511","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749511","url":null,"abstract":"In Europe there are more than 580 000 people who suffer from aphasia - an acquired speech and language disorder that occurs because of brain damage, primarily as a result of a stroke. Especially with regards to demographic change, health care systems have to face present and future challenges to improve aphasia therapy. Thereby, immediate therapeutic measures are decisive for best possible and long-term success in language therapy. Regarding essential requirements, on the one hand, therapy intensity and frequency have to be increased significantly while on the other hand, measures need to be adjusted along everyday activities. A very promising approach to meet this requirements are augmented reality applications. They can be used to create a highly natural exercise situation, in which patients interact and practice with their personal possessions at home. This facilitates the successful and continuous transfer of learnings for the patient, contrary to being solely dependent on clinical therapy units. This paper gives an overview of the concept of a real-time software providing augmented and dynamic language therapy, which is interactive and utilizes simple user interface design, for home-based training.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"26 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":"124203361","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.2016.7749469
Georges Matar, J. Lina, J. Carrier, Anna Riley, Georges Kaddoum
In this paper, we propose an Internet of Things (IoT) system application for remote medical monitoring. The body pressure distribution is acquired through a pressure sensing mattress under the person's body, data is sent to a computer workstation for processing, and results are communicated for monitoring and diagnosis. The area of application of such system is large in the medical domain making the system convenient for clinical use such as in sleep studies, non or partial anesthetic surgical procedures, medical-imaging techniques, and other areas involving the determination of the body-posture on a mattress. In this vein, a novel method for human body posture recognition that consists in providing an optimal combination of signal acquisition, processing, and data storage to perform the recognition task in a quasi-real-time basis. A supervised learning approach was used to build a model using a robust synthetic data. The data has been generated beforehand, in a way to enhance and generalize the recognition capability while maintaining both geometrical and spatial performance. Low-cost and fast computation per sample processing along with autonomy, make the system suitable for long-term operation and IoT applications. The recognition results with a Cohen's Kappa coefficient κ = 0.866 was satisfactorily encouraging for further investigation in this field.
{"title":"Internet of Things in sleep monitoring: An application for posture recognition using supervised learning","authors":"Georges Matar, J. Lina, J. Carrier, Anna Riley, Georges Kaddoum","doi":"10.1109/HealthCom.2016.7749469","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749469","url":null,"abstract":"In this paper, we propose an Internet of Things (IoT) system application for remote medical monitoring. The body pressure distribution is acquired through a pressure sensing mattress under the person's body, data is sent to a computer workstation for processing, and results are communicated for monitoring and diagnosis. The area of application of such system is large in the medical domain making the system convenient for clinical use such as in sleep studies, non or partial anesthetic surgical procedures, medical-imaging techniques, and other areas involving the determination of the body-posture on a mattress. In this vein, a novel method for human body posture recognition that consists in providing an optimal combination of signal acquisition, processing, and data storage to perform the recognition task in a quasi-real-time basis. A supervised learning approach was used to build a model using a robust synthetic data. The data has been generated beforehand, in a way to enhance and generalize the recognition capability while maintaining both geometrical and spatial performance. Low-cost and fast computation per sample processing along with autonomy, make the system suitable for long-term operation and IoT applications. The recognition results with a Cohen's Kappa coefficient κ = 0.866 was satisfactorily encouraging for further investigation in this field.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"1 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":"129768318","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.2016.7749420
Erick Ribeiro, Larissa Bentes, Anderson Cruz, Gabriel Leitão, R. Barreto, V. Silva, T. Primo, F. Koch
This paper depicts a machine learning method for fainting and epileptic seizures automatic recognition. We evaluated five machine learning techniques in order to find out which classification method maximizes the accuracy level and, at the same time, minimizes the computational complexity since the experimental environment has very limited computational resources (processing power). We prototype such method in a wearable device, taking into account F-Score and Accuracy metrics. The experimental evaluation shows that there are no significant difference between KNN, PART, and C4.5. However, KNN has high computational cost when compared to PART and C4.5. PART has low computational cost when compared to C4.5 since it identified less rules.
{"title":"On the use of inertial sensors and machine learning for automatic recognition of fainting and epileptic seizure","authors":"Erick Ribeiro, Larissa Bentes, Anderson Cruz, Gabriel Leitão, R. Barreto, V. Silva, T. Primo, F. Koch","doi":"10.1109/HealthCom.2016.7749420","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749420","url":null,"abstract":"This paper depicts a machine learning method for fainting and epileptic seizures automatic recognition. We evaluated five machine learning techniques in order to find out which classification method maximizes the accuracy level and, at the same time, minimizes the computational complexity since the experimental environment has very limited computational resources (processing power). We prototype such method in a wearable device, taking into account F-Score and Accuracy metrics. The experimental evaluation shows that there are no significant difference between KNN, PART, and C4.5. However, KNN has high computational cost when compared to PART and C4.5. PART has low computational cost when compared to C4.5 since it identified less rules.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"53 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":"117275360","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.2016.7749530
J. Fernandes, H. Dinis, Luís M. Gonçalves, P. Mendes
Medication resistant neurological and psychiatric disorders, RNPD, are devastating multicausal chronic diseases that cannot be adequately controlled using conventional pharmaco and/or psychotherapies, being epilepsy a well-known RNPD. Wireless biomedical device availability is growing at an impressive rate, and the systems' miniaturization, integration and complexity is also increasing, unveiling new therapies based on such new devices. This paper presents a new wireless implantable device as a solution for thermal neuromodulation of brain cells, which can be used to treat or study the brain's behavior when cooled down. The obtained results show that, despite these systems' potential to be power hungry, they may operate within acceptable electrical power values, while reaching the required neuromodulation temperatures.
{"title":"Implantable microdevice with integrated wireless power transfer for thermal neuromodulation applications","authors":"J. Fernandes, H. Dinis, Luís M. Gonçalves, P. Mendes","doi":"10.1109/HealthCom.2016.7749530","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749530","url":null,"abstract":"Medication resistant neurological and psychiatric disorders, RNPD, are devastating multicausal chronic diseases that cannot be adequately controlled using conventional pharmaco and/or psychotherapies, being epilepsy a well-known RNPD. Wireless biomedical device availability is growing at an impressive rate, and the systems' miniaturization, integration and complexity is also increasing, unveiling new therapies based on such new devices. This paper presents a new wireless implantable device as a solution for thermal neuromodulation of brain cells, which can be used to treat or study the brain's behavior when cooled down. The obtained results show that, despite these systems' potential to be power hungry, they may operate within acceptable electrical power values, while reaching the required neuromodulation temperatures.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"181 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":"124544658","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.2016.7749454
Vasilios A. Keramaris, K. Danas
Nowadays, more than ever, it is evident that we need a series of technological and methodological techniques in order to improve on the systems analysis and design (SAD) of any informational system. Recently, there is a huge demand to create domain vocabularies and semantics that along with cognition are to describe the information of any domain in the form of Resource Description Framework (RDF) and ontologies (OWL), both types of data models, resulting into relational and directed graphs and as a result both humans and machines simultaneously can understand the information offered, with machines using online links and pattern matching in order to interpret the meaning of it in a consistent and meaningful way. Ontologies are great ensuring interopirability and consistency of data, however in order to improve on current Information Systems (IS) such as Hospital Information Systems (HIS), there are many other methodologies that need to be invoked. This could be achieved with the waterfall approach and multiple deployment of systems analysis and design methodologies and of course detailed computational ontologies that will be used as a basis to represent and share domain specific knowledge and data structures ensuring the interoperability of systems and the quality of information within that domain. This multimethodological systems analysis and design framework, named “OntoDrive” is presented here.
{"title":"\"OntoDrive\" A multi-methodological ontology driven framework for systems analysis of health informatics","authors":"Vasilios A. Keramaris, K. Danas","doi":"10.1109/HealthCom.2016.7749454","DOIUrl":"https://doi.org/10.1109/HealthCom.2016.7749454","url":null,"abstract":"Nowadays, more than ever, it is evident that we need a series of technological and methodological techniques in order to improve on the systems analysis and design (SAD) of any informational system. Recently, there is a huge demand to create domain vocabularies and semantics that along with cognition are to describe the information of any domain in the form of Resource Description Framework (RDF) and ontologies (OWL), both types of data models, resulting into relational and directed graphs and as a result both humans and machines simultaneously can understand the information offered, with machines using online links and pattern matching in order to interpret the meaning of it in a consistent and meaningful way. Ontologies are great ensuring interopirability and consistency of data, however in order to improve on current Information Systems (IS) such as Hospital Information Systems (HIS), there are many other methodologies that need to be invoked. This could be achieved with the waterfall approach and multiple deployment of systems analysis and design methodologies and of course detailed computational ontologies that will be used as a basis to represent and share domain specific knowledge and data structures ensuring the interoperability of systems and the quality of information within that domain. This multimethodological systems analysis and design framework, named “OntoDrive” is presented here.","PeriodicalId":167022,"journal":{"name":"2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"2011 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":"121327757","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：