Pub Date : 2014-10-01DOI: 10.1109/HealthCom.2014.7001866
C. Rondinoni, V. Souza, R. Matsuda, A. Peres, M. Santos, O. B. Filho, A. C. Santos, H. Machado, P. Noritomi, Jorge Silva
This study is the first step in an effort to develop three-dimensional (3D) printing for use in pediatric surgical planning. In order to accomplish this, we established an effective collaboration between Ribeirao Preto Clinics Hospital (HCRP) and Renato Archer Center for Information Technology (CTI). Printed biomodels can be used to support discussions, decision-making, and neuronavigation before surgery. The main purpose of 3D printing for specific case handling is to reduce damage by enhancing knowledge of orientation during surgical planning and personnel training before surgery. Here, we produced an object that represented the brain and face segment of a patient via additive manufacturing technology based on magnetic resonance imaging (MRI) data. Specific landmarks were measured by three distinct methods: manual caliper, an InVesalius software measurement tool, and neuronavigation coordinate detection. The mean coefficient of variation was 7.17% between all methods and landmarks measured. Our results validate the combined use of biomodels with InVesalius software tools for the assessment of individual brain anatomy facilitating manual handling and visualization of 3D models. The establishment of communication protocols between the teams involved, as well as navigation protocols for quality control, presents the possibility of developing long term training programs, and promotes the congregation of individuals from research areas in Medical Physics, Medical Sciences, and Neuroscience.
{"title":"Inter-institutional protocol describing the use of three-dimensional printing for surgical planning in a patient with childhood epilepsy: From 3D modeling to neuronavigation","authors":"C. Rondinoni, V. Souza, R. Matsuda, A. Peres, M. Santos, O. B. Filho, A. C. Santos, H. Machado, P. Noritomi, Jorge Silva","doi":"10.1109/HealthCom.2014.7001866","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001866","url":null,"abstract":"This study is the first step in an effort to develop three-dimensional (3D) printing for use in pediatric surgical planning. In order to accomplish this, we established an effective collaboration between Ribeirao Preto Clinics Hospital (HCRP) and Renato Archer Center for Information Technology (CTI). Printed biomodels can be used to support discussions, decision-making, and neuronavigation before surgery. The main purpose of 3D printing for specific case handling is to reduce damage by enhancing knowledge of orientation during surgical planning and personnel training before surgery. Here, we produced an object that represented the brain and face segment of a patient via additive manufacturing technology based on magnetic resonance imaging (MRI) data. Specific landmarks were measured by three distinct methods: manual caliper, an InVesalius software measurement tool, and neuronavigation coordinate detection. The mean coefficient of variation was 7.17% between all methods and landmarks measured. Our results validate the combined use of biomodels with InVesalius software tools for the assessment of individual brain anatomy facilitating manual handling and visualization of 3D models. The establishment of communication protocols between the teams involved, as well as navigation protocols for quality control, presents the possibility of developing long term training programs, and promotes the congregation of individuals from research areas in Medical Physics, Medical Sciences, and Neuroscience.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127430968","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 : 2014-10-01DOI: 10.1109/HEALTHCOM.2014.7001823
Isaac Golden, A. Stranieri, T. Sahama, S. Pilapitiya, Sisira Siribaddana, Stephen Vaughan
Culturally, philosophically and religiously diverse medical systems including Western medicine, Traditional Chinese Medicine, Ayurvedic Medicine and Homeopathic Medicine, once situated in places and times relatively unconnected from each other, currently co-exist to a point where patients must choose which system to consult. These decisions require comparative analyses, yet the divergence in key underpinning assumptions is so great that comparisons cannot easily be made. However, diverse medical systems can be meaningfully juxtaposed for the purpose of making practical decisions if relevant information is presented appropriately. Information regarding privacy provisions inherent in the typical practice of each medical system is an important element in this juxtaposition. In this paper the information needs of patients making decisions regarding the selection of a medical system, are examined.
{"title":"Informatics to support patient choice between diverse medical systems","authors":"Isaac Golden, A. Stranieri, T. Sahama, S. Pilapitiya, Sisira Siribaddana, Stephen Vaughan","doi":"10.1109/HEALTHCOM.2014.7001823","DOIUrl":"https://doi.org/10.1109/HEALTHCOM.2014.7001823","url":null,"abstract":"Culturally, philosophically and religiously diverse medical systems including Western medicine, Traditional Chinese Medicine, Ayurvedic Medicine and Homeopathic Medicine, once situated in places and times relatively unconnected from each other, currently co-exist to a point where patients must choose which system to consult. These decisions require comparative analyses, yet the divergence in key underpinning assumptions is so great that comparisons cannot easily be made. However, diverse medical systems can be meaningfully juxtaposed for the purpose of making practical decisions if relevant information is presented appropriately. Information regarding privacy provisions inherent in the typical practice of each medical system is an important element in this juxtaposition. In this paper the information needs of patients making decisions regarding the selection of a medical system, are examined.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131619051","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001835
Yi Liu, Chonho Lee, Bu-Sung Lee, James K. R. Stevenson, M. McKeown
Recent studies have suggested significant differences in motor performances of Parkinson's Disease (PD) patients who have L-dopa induced dyskinesias (LIDs), even when off of L-dopa medication. The pathophysiology of LIDs remains obscure, so applying data-mining techniques to the patients' motor performance may provide some heuristic insight. This paper investigated visually-guided tracking performance of PD patients using data mining techniques to reveal the differences between dyskinesia and non-dyskinesia patients. We found that K-means clustering of the root mean square (RMS) tracking error at faster tracking speeds and with ambiguous visual stimuli was able to effectively discriminate between the two groups with 77.8% accuracy. Decision tree classification was less accurate (68.4%) and determined that years since diagnosis was the best feature to distinguish between groups. Our results suggest that data mining methodologies may provide novel insights into features of the neurovegetative disease.
{"title":"Analysis of visually guided tracking performance in Parkinson's disease","authors":"Yi Liu, Chonho Lee, Bu-Sung Lee, James K. R. Stevenson, M. McKeown","doi":"10.1109/HealthCom.2014.7001835","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001835","url":null,"abstract":"Recent studies have suggested significant differences in motor performances of Parkinson's Disease (PD) patients who have L-dopa induced dyskinesias (LIDs), even when off of L-dopa medication. The pathophysiology of LIDs remains obscure, so applying data-mining techniques to the patients' motor performance may provide some heuristic insight. This paper investigated visually-guided tracking performance of PD patients using data mining techniques to reveal the differences between dyskinesia and non-dyskinesia patients. We found that K-means clustering of the root mean square (RMS) tracking error at faster tracking speeds and with ambiguous visual stimuli was able to effectively discriminate between the two groups with 77.8% accuracy. Decision tree classification was less accurate (68.4%) and determined that years since diagnosis was the best feature to distinguish between groups. Our results suggest that data mining methodologies may provide novel insights into features of the neurovegetative disease.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132505615","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001886
S. Hara, T. Tsujioka, Takunori Shimazaki, Kouhei Tezuka, Masayuki Ichikawa, Masato Ariga, Hajime Nakamura, Takashi Kawabata, Kenji Watanabe, M. Ise, Noa Arime, H. Okuhata
We have developed a real-time vital signs monitoring system for two years in 2012 and 2013. Just by putting a single vital sensor node to the back waist position of each player and placing four data collection nodes around a field, the system can monitor at a note PC heart rate (HR), energy expenditure (EE) and body temperature (BT) for all players during a football game in real-time, periodically and reliably. The system is based on novel vital sensing technique and wireless data transmission technique. This paper introduces the two techniques in the system, presents some problems encountered in the system development and discusses solutions for them.
{"title":"Elements of a real-time vital signs monitoring system for players during a football game","authors":"S. Hara, T. Tsujioka, Takunori Shimazaki, Kouhei Tezuka, Masayuki Ichikawa, Masato Ariga, Hajime Nakamura, Takashi Kawabata, Kenji Watanabe, M. Ise, Noa Arime, H. Okuhata","doi":"10.1109/HealthCom.2014.7001886","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001886","url":null,"abstract":"We have developed a real-time vital signs monitoring system for two years in 2012 and 2013. Just by putting a single vital sensor node to the back waist position of each player and placing four data collection nodes around a field, the system can monitor at a note PC heart rate (HR), energy expenditure (EE) and body temperature (BT) for all players during a football game in real-time, periodically and reliably. The system is based on novel vital sensing technique and wireless data transmission technique. This paper introduces the two techniques in the system, presents some problems encountered in the system development and discusses solutions for them.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134362465","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001806
K. Chelette, Cheryl Carrico, L. Nichols, Emily Salyers, L. Sawaki
Transcranial direct current stimulation (tDCS) is a form of non-invasive brain stimulation that can modulate neuroplasticity (the capacity for brain reorganization). Neuroplastic change correlates with upper extremity (UE) recovery after brain lesions. Different electrode configurations of tDCS paired with UE motor training can have different effects in distinct populations. We are conducting the first randomized, double-blind, placebo-controlled trial to investigate which tDCS configuration may best enhance outcomes of UE motor training for stroke survivors with chronic, severe hemiparesis (i.e., little or no wrist or hand movement). We have assigned subjects to 1 of 4 groups: 1) “Anodal”: anodal tDCS to excite ipsilesional motor cortex; 2) “Cathodal”: cathodal tDCS to inhibit contralesional motor cortex; 3) “Dual”: a simultaneous combination of anodal and cathodal tDCS; or 4) “Sham” tDCS. Intervention (10 sessions) consists of tDCS followed by 3 hours of intensive, task-oriented UE training in each session. Our primary outcome measure is Fugl-Meyer Assessment. Our secondary outcome measures are Action Research Arm Test and Stroke Impact Scale. We have conducted evaluations at baseline and post-intervention. Preliminary results from 26 of (projected) 44 subjects indicate substantially greater improvement for the “Cathodal” group than other groups. These findings differ from evidence about tDCS in rehabilitation of mild-to-moderate hemiparesis. Completion of our study will include full analysis of neuroplastic change associated with intervention.
{"title":"Effects of electrode configurations in transcranial direct current stimulation after stroke","authors":"K. Chelette, Cheryl Carrico, L. Nichols, Emily Salyers, L. Sawaki","doi":"10.1109/HealthCom.2014.7001806","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001806","url":null,"abstract":"Transcranial direct current stimulation (tDCS) is a form of non-invasive brain stimulation that can modulate neuroplasticity (the capacity for brain reorganization). Neuroplastic change correlates with upper extremity (UE) recovery after brain lesions. Different electrode configurations of tDCS paired with UE motor training can have different effects in distinct populations. We are conducting the first randomized, double-blind, placebo-controlled trial to investigate which tDCS configuration may best enhance outcomes of UE motor training for stroke survivors with chronic, severe hemiparesis (i.e., little or no wrist or hand movement). We have assigned subjects to 1 of 4 groups: 1) “Anodal”: anodal tDCS to excite ipsilesional motor cortex; 2) “Cathodal”: cathodal tDCS to inhibit contralesional motor cortex; 3) “Dual”: a simultaneous combination of anodal and cathodal tDCS; or 4) “Sham” tDCS. Intervention (10 sessions) consists of tDCS followed by 3 hours of intensive, task-oriented UE training in each session. Our primary outcome measure is Fugl-Meyer Assessment. Our secondary outcome measures are Action Research Arm Test and Stroke Impact Scale. We have conducted evaluations at baseline and post-intervention. Preliminary results from 26 of (projected) 44 subjects indicate substantially greater improvement for the “Cathodal” group than other groups. These findings differ from evidence about tDCS in rehabilitation of mild-to-moderate hemiparesis. Completion of our study will include full analysis of neuroplastic change associated with intervention.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126359540","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001883
Medina Hadjem, Osman Salem, Farid Naït-Abdesselam
Cardiovascular diseases (CVD) are known to be the most widespread causes to death. Therefore, detecting earlier signs of cardiac anomalies is of prominent importance to ease the treatment of any cardiac complication or take appropriate actions. Electrocardiogram (ECG) is used by doctors as an important diagnosis tool and in most cases, it's recorded and analyzed at hospital after the appearance of first symptoms or recorded by patients using a device named holter ECG and analyzed afterward by doctors. In fact, there is a lack of systems able to capture ECG and analyze it remotely before the onset of severe symptoms. With the development of wearable sensor devices having wireless transmission capabilities, there is a need to develop real time systems able to accurately analyze ECG and detect cardiac abnormalities. In this paper, we propose a new CVD detection system using Wireless Body Area Networks (WBAN) technology. This system processes the captured ECG using filtering and Undecimated Wavelet Transform (UWT) techniques to remove noises and extract nine main ECG diagnosis parameters, then the system uses a Bayesian Network Classifier model to classify ECG based on its parameters into four different classes: Normal, Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC) and Myocardial Infarction (MI). The experimental results on ECGs from real patients databases show that the average detection rate (TPR) is 96.1% for an average false alarm rate (FPR) of 1.3%.
{"title":"An ECG monitoring system for prediction of cardiac anomalies using WBAN","authors":"Medina Hadjem, Osman Salem, Farid Naït-Abdesselam","doi":"10.1109/HealthCom.2014.7001883","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001883","url":null,"abstract":"Cardiovascular diseases (CVD) are known to be the most widespread causes to death. Therefore, detecting earlier signs of cardiac anomalies is of prominent importance to ease the treatment of any cardiac complication or take appropriate actions. Electrocardiogram (ECG) is used by doctors as an important diagnosis tool and in most cases, it's recorded and analyzed at hospital after the appearance of first symptoms or recorded by patients using a device named holter ECG and analyzed afterward by doctors. In fact, there is a lack of systems able to capture ECG and analyze it remotely before the onset of severe symptoms. With the development of wearable sensor devices having wireless transmission capabilities, there is a need to develop real time systems able to accurately analyze ECG and detect cardiac abnormalities. In this paper, we propose a new CVD detection system using Wireless Body Area Networks (WBAN) technology. This system processes the captured ECG using filtering and Undecimated Wavelet Transform (UWT) techniques to remove noises and extract nine main ECG diagnosis parameters, then the system uses a Bayesian Network Classifier model to classify ECG based on its parameters into four different classes: Normal, Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC) and Myocardial Infarction (MI). The experimental results on ECGs from real patients databases show that the average detection rate (TPR) is 96.1% for an average false alarm rate (FPR) of 1.3%.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130631721","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001899
Irshad Faiz, H. Mukhtar, Sharifullah Khan
Diabetes is among one of the fastest growing disease all over the world. Controlled diet and proper exercise are considered as a treatment to control diabetes. However, food and exercise suggestions in existing solutions do not consider integrated knowledge from personal profile, preferences, current vital signs, diabetes domain, food domain and exercise domain. Furthermore, there is a strong correlation of diet and exercise. We have implemented an ontology based integrated approach to combine knowledge from various domains to generate diet and exercise suggestions for diabetics. The solution is developed as a Semantic Healthcare Assistant for Diet and Exercise (SHADE). For each domain (person, diabetes, food and exercise) we have defined separate ontology along with rules and then an integrated ontology combines these individual ontologies. Finally, diet recommendations are presented in the form of various alternative menus such that each menu is a healthy and balanced diet.
{"title":"An integrated approach of diet and exercise recommendations for diabetes patients","authors":"Irshad Faiz, H. Mukhtar, Sharifullah Khan","doi":"10.1109/HealthCom.2014.7001899","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001899","url":null,"abstract":"Diabetes is among one of the fastest growing disease all over the world. Controlled diet and proper exercise are considered as a treatment to control diabetes. However, food and exercise suggestions in existing solutions do not consider integrated knowledge from personal profile, preferences, current vital signs, diabetes domain, food domain and exercise domain. Furthermore, there is a strong correlation of diet and exercise. We have implemented an ontology based integrated approach to combine knowledge from various domains to generate diet and exercise suggestions for diabetics. The solution is developed as a Semantic Healthcare Assistant for Diet and Exercise (SHADE). For each domain (person, diabetes, food and exercise) we have defined separate ontology along with rules and then an integrated ontology combines these individual ontologies. Finally, diet recommendations are presented in the form of various alternative menus such that each menu is a healthy and balanced diet.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"46 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114460609","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001808
C. S. Deolindo, A. Kunicki, F. Brasil, R. Moioli
The anatomical and functional characterization of neuronal assemblies (NAs) is a major challenge in neuroscience. Principal component analysis (PCA) is a widely used method for feature detection, however, when dealing with neuronal data analysis, its limitations have not yet been fully understood. Our work complements previous PCA studies which, in general, characterise NAs based solely on excitatory neuronal interactions. We analysed the performance of PCA in two neglected scenarios: assemblies containing patterns of neural interactions (1) with inhibition and (2) with delays. The analyses considered two types of artificially generated data, one drawn from a traditional Poissonian model, and the other drawn from a latent multivariate Gaussian model; in both models, data from a behaving Wistar rat was used for parameter tuning. Our results highlight scenarios in which neglecting complex interactions between neurons can lead to false conclusions when using PCA to detect NAs. Also, we reinforce the importance of more realistic simulations in the evaluation of neuronal signal processing algorithms.
{"title":"Limitations of principal component analysis as a method to detect neuronal assemblies","authors":"C. S. Deolindo, A. Kunicki, F. Brasil, R. Moioli","doi":"10.1109/HealthCom.2014.7001808","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001808","url":null,"abstract":"The anatomical and functional characterization of neuronal assemblies (NAs) is a major challenge in neuroscience. Principal component analysis (PCA) is a widely used method for feature detection, however, when dealing with neuronal data analysis, its limitations have not yet been fully understood. Our work complements previous PCA studies which, in general, characterise NAs based solely on excitatory neuronal interactions. We analysed the performance of PCA in two neglected scenarios: assemblies containing patterns of neural interactions (1) with inhibition and (2) with delays. The analyses considered two types of artificially generated data, one drawn from a traditional Poissonian model, and the other drawn from a latent multivariate Gaussian model; in both models, data from a behaving Wistar rat was used for parameter tuning. Our results highlight scenarios in which neglecting complex interactions between neurons can lead to false conclusions when using PCA to detect NAs. Also, we reinforce the importance of more realistic simulations in the evaluation of neuronal signal processing algorithms.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127820207","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001821
Carlos Pereira, S. Frade, P. Brandão, R. Correia, Ana Aguiar
E-health has raised a great deal of expectations on improving the quality of health services while simultaneously enabling health services cost reductions. To advance towards those visions, it is imperative to gain the trust of the involved stakeholders, doctors and other medical personnel, patients, families, health care providers and regulators. Even though one critical requirement is interoperability among the various systems involved, currently existing solutions are still vertical silos to a large extent. In this paper, we present an E-health solution that results from the integration of components that comply with rising standards at the various levels of the ICT infrastructure: Machine-to-Machine (M2M) communications for interconnecting devices and services, Health Level 7 (HL7) for communicating with health platforms and openEHR for data semantics, storing and making data available. Concretely, we provide an interoperable and extensible e-health service following these three uprising standards and present the architecture design. We map the service to the various components of the infrastructure building blocks, thus demonstrating how the integration can be successfully accomplished. We are currently developing a prototype solution to be used in a pilot project with 15 elders.
{"title":"Integrating data and network standards into an interoperable e-Health solution","authors":"Carlos Pereira, S. Frade, P. Brandão, R. Correia, Ana Aguiar","doi":"10.1109/HealthCom.2014.7001821","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001821","url":null,"abstract":"E-health has raised a great deal of expectations on improving the quality of health services while simultaneously enabling health services cost reductions. To advance towards those visions, it is imperative to gain the trust of the involved stakeholders, doctors and other medical personnel, patients, families, health care providers and regulators. Even though one critical requirement is interoperability among the various systems involved, currently existing solutions are still vertical silos to a large extent. In this paper, we present an E-health solution that results from the integration of components that comply with rising standards at the various levels of the ICT infrastructure: Machine-to-Machine (M2M) communications for interconnecting devices and services, Health Level 7 (HL7) for communicating with health platforms and openEHR for data semantics, storing and making data available. Concretely, we provide an interoperable and extensible e-health service following these three uprising standards and present the architecture design. We map the service to the various components of the infrastructure building blocks, thus demonstrating how the integration can be successfully accomplished. We are currently developing a prototype solution to be used in a pilot project with 15 elders.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412653","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 : 2014-10-01DOI: 10.1109/HealthCom.2014.7001869
L. Minelli, M. C. d'Ornellas, Ana T. Winck
The gradual increase of cancer cases worldwide has been posing a need on the use of computing resources to accurately retrieve the information recorded in databases. One can highlight the retrieved information importance from a specialist in order to better evaluate pathological response and predict the cancer patient prognosis. This paper presents a way to represent knowledge of cancer registries with emphasis on prognosis. It makes use data mining techniques to find patterns in data stored for patient's lifetime in similar situations. The work is focused on the generation of association rules to find patterns on these registries in order to measure the patient prognosis and drive healthcare experts conclusions. A validation against international oncology organizations and health publications was also made to ensure data and work reliability.
{"title":"Knowledge representation for lung cancer patients' prognosis","authors":"L. Minelli, M. C. d'Ornellas, Ana T. Winck","doi":"10.1109/HealthCom.2014.7001869","DOIUrl":"https://doi.org/10.1109/HealthCom.2014.7001869","url":null,"abstract":"The gradual increase of cancer cases worldwide has been posing a need on the use of computing resources to accurately retrieve the information recorded in databases. One can highlight the retrieved information importance from a specialist in order to better evaluate pathological response and predict the cancer patient prognosis. This paper presents a way to represent knowledge of cancer registries with emphasis on prognosis. It makes use data mining techniques to find patterns in data stored for patient's lifetime in similar situations. The work is focused on the generation of association rules to find patterns on these registries in order to measure the patient prognosis and drive healthcare experts conclusions. A validation against international oncology organizations and health publications was also made to ensure data and work reliability.","PeriodicalId":269964,"journal":{"name":"2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126896377","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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