DESIGNING AN ARCHITECTURE FOR MONITORING PATIENTS AT HOME ONTOLOGIES AND WEB SERVICES FOR CLINICAL AND TECHNICAL MANAGEMENT INTEGRATION
By
A
PROJECT REPORT
Submitted to the Department of Computer Science & Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY
In partial fulfillment of the requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
IN
COMPUTER SCIENCE & ENGINEERING
APRIL 2015
CERTIFICATE
Certified that this project report titled “Designing an Architecture for Monitoring Patients at Home Ontologies and Web Services for Clinical and Technical Management Integration” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate.
Signature of the Guide Signature of the H.O.D
Name Name
DECLARATION
I hereby declare that the project work entitled “Designing an Architecture for Monitoring Patients at Home Ontologies and Web Services for Clinical and Technical Management Integration” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF SCIENCE IN COMPUTER SCIENCE is a record of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate.
(Student Name)
(Reg.No)
Place:
Date:
ACKNOWLEDGEMENT
I am extremely glad to present my project “Designing an Architecture for Monitoring Patients at Home Ontologies and Web Services for Clinical and Technical Management Integration” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work.
I would like to express my Director, Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.) of who had given me an opportunity to undertake this project.
I am highly indebted to Co-Ordinator Prof. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project.
I wish to express my
deep sense of gratitude to my guide
Prof. A.Vinayagam M.Sc., M.Phil., M.E., for
her immense help and encouragement for successful completion of this project.
I also express my sincere thanks to the all the staff members of Computer science for their kind advice.
And last, but not the
least, I express my deep gratitude to my parents and friends for their
encouragement and support throughout the project.
CHAPTER 1
This paper presents the design and implementation of an architecture based on the combination of ontologies, rules, web services, and the autonomic computing paradigm to manage data in home-based telemonitoring scenarios.
The architecture includes two layers: 1) a conceptual layer and 2) a data and communication layer. On the one hand, the conceptual layer based on ontologies is proposed to unify the management procedure and integrate incoming data from all the sources involved in the telemonitoring process. On the other hand, the data and communication layer based on REST web service (WS) technologies is proposed to provide practical backup to the use of the ontology, to provide a real implementation of the tasks it describes and thus to provide a means of exchanging data (support communication tasks).
We study
regarding chronic obstructive pulmonary disease data management is presented in
order to evaluate the efficiency of the architecture. This proposed
ontology-based solution defines a flexible and scalable architecture in order
to address main challenges presented in home-based telemonitoring scenarios and
thus provide a means to integrate, unify, and transfer data supporting both clinical
and technical management tasks.
1.2 INTRODUCTION
Patient empowerment is considered as a philosophy of health care based on the perspective that better outcomes are achieved when patients become active participants in their own health management. This new paradigm is a central idea in the European Union (EU) health strategy supported by international health organizations including the World Health Organization among others, and its effectiveness in yielding quality of care is an obvious and essential area of research. This new idea invites to look for new ways of providing healthcare, e.g., by using information and communications technologies. In this context, home-based telemonitoring systems can be used as self-care management tools, while collaborative processes among healthcare personnel and patients are maintained, thus the patient’s safe control is guaranteed. Telemonitoring systems face the problem of delivering medicine to the current growing population with chronic conditions while at the same time covering the dimensions of quality of care and new paradigms such as empowerment can be supported.
By periodically collecting patients’
themselves clinical data (located at their home sites) and transferring them to
physicians located in remote sites, patient’s health status supervision and
feedback provision are possible. This type of telemedicine system guarantees
patient control while reducing costs and avoiding hospital overflows. These two
sites (home site and healthcare site) comprised a typical home-based
telemonitoring system. At home site, data acquired by using MDs together with
the patient’s feedback are collected in a concentrator device (HG) used to
evaluate and/or transfer the acquired data outside the patient’s home if
necessary. At the health-care site, a server device is used to manage information
from the home site as well as to manage and store the patient’s monitoring
guidelines defined by physicians (TS, telemonitoring server). In fact, this
telemonitoring process, and consequently the evolution of the patient’s health
status, ismanaged through the indications or monitoring guidelines provided by
physicians.
Although significant contributions have been made in this field in recent decades, telemedicine and in e-health scenarios in general still pose numerous challenges that need to be addressed by researchers in order to take maximum advantage of the benefits that these systems provide and to support their long-term implementation. Interoperability and integration are critical challenges that also need to be addressed when developing monitoring systems in order to provide effective healthcare and to make possible seamless communication among the different heterogeneous health entities that participate in the monitoring process. This integration should be addressed at both end sites of the scenario but also in the communication link, thus integrating the way of transferring and exchanging information efficiently between them.
We providing personalized care services and taking into account the patient’s context have been identified as additional requirements. Furthermore, apart from clinical data aspects, technical issues should be also addressed in this scenario. Technical management of all the devices that comprise the telemonitoring scenario (e.g., the MDs and HG) is an important task that may or may not be integrated under the same architecture as clinical management. Hence, at this technical level, research is still required to address these challenges. Consequently there is a need for the development of new telemonitoring architectures.
Great efforts have been made in recent years in developing standards to deal with interoperability at different points of the e-health communication infrastructure such as the ISO/IEEE 11073 (X73) for MDs interoperability, the OpenEHR initiative for storage, management and retrieval of electronic health record (EHR) information or as the standardized Health Level Seven7 (HL7) messages to solve clinical data transferences. Nevertheless, additional efforts are required to enable them to work together and ultimately provide a higher level of integration.
Specifically, in this telemonitoring scenario, there is not a unique standard-based solution to address data and management integration. Since several standards can be used (some of them in combination with proprietary protocols or other standards) at different points of this scenario, the interoperability problem remains unsolved unless these standards would merge into one or alignments and combination of them would be done. According to Berges et al. interoperability does not mean to have a unique representation but a semantically acknowledged equivalent one. That is the reason to propose in this study an ontology-based architecture in order to provide with a common knowledge about the exchanged data and the management of such data. This ontology constitutes the knowledge equivalent one. Then, at both ends of the architecture other standards could be used for other managing purposes relating this model with the specific desired approach. Using this alternative, a knowledge model is first provided that avoids alignment of models two by two, while all being related through the main ontology.
Ontologies-based solutions have been popularized over the past few years. Ontologies provide a higher level of abstraction and have been successfully used in telemonitoring scenarios and other areas to provide knowledge representation and semantic integration, thus a common understanding about data exchanged by all the entities. Furthermore, its combination with rules allows providing personalized management services and thus personalized care. Although there are works that describe the details of an ontology approach in this domain, they do not devote much attention to the architecture implementation and the communication used to exchange the information described. Consequently, fewworks have given details about this practical implementation of the ontology-based system which may be of interest for the development of other ontology-based applications in and outside the e-health domain.
This paper presents an ontology-driven
architecture to integrate data management and enable its communication in a
telemonitoring scenario. The proposed architecture includes two layers: the
conceptual layer (the ontology) and the communication and data layer. The
conceptual layer uses the HOTMES and its extensions introduced. Specifically,
the OWL-DL language was selected to define this ontology model. The second
layer is based on WS technologies. WSs have been successfully used in network
management and also in other works to exchange data modeled by ontology.
However, our proposal, inspired on the representational state transfer (REST)
style and based on a generic communication method, provides a different design
approach that may be reusable for other systems based on ontologies.
Furthermore, security issues have been considered. The aim is to define a
flexible and scalable architecture in order to address main challenges
presented in home-based telemonitoring scenarios and thus provide a means to
integrate and transfer data supporting both clinical and technical data
management.
1.3 LITRATURE SURVEY
AUTHOR AND PUBLICATION: JD. Trigo, I. Mart´ınez, A. Alesanco, A. Kollmann, J. Escayola, D. Hayn, G. Schreier, and J. Garc´ıa, “AN INTEGRATED HEALTHCARE INFORMATION SYSTEM FOR END-TO-END STANDARDIZED EXCHANGE AND HOMOGENEOUS MANAGEMENT OF DIGITAL ECG FORMATS,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 518–529, Jul. 2012.
EXPLANATION:
This paper investigates the application of the enterprise information system (EIS) paradigm to standardized cardiovascular condition monitoring. There are many specifications in cardiology, particularly in the ECG standardization arena. The existence of ECG formats, however, does not guarantee the implementation of homogeneous, standardized solutions for ECG management. In fact, hospital management services need to cope with various ECG formats and, moreover, several different visualization applications. This heterogeneity hampers the normalization of integrated, standardized healthcare information systems, hence the need for finding an appropriate combination of ECG formats and suitable EIS-based software architecture that enables standardized exchange and homogeneous management of ECG formats. Determining such a combination is one objective of this paper.
We develop the integrated healthcare information system that satisfies the requirements posed by the previous determination. The ECG formats selected include ISO/IEEE11073, Standard Communications Protocol for Computer-Assisted Electrocardiography, and an ECG ontology. The EIS-enabling techniques and technologies selected include web services, simple object access protocol, extensible markup language, or business process execution language. Such a selection ensures the standardized exchange of ECGs within, or across, healthcare information systems while providing modularity and accessibility.
AUTHOR AND PUBLICATION: D. Ria˜no, F. Real, J. A. L´opez-Vallverd´u, F. Campana, S. Ercolani, P. Mecocci, R. Annicchiarico, and C. Caltagirone, “AN ONTOLOGY-BASED PERSONALIZATION OF HEALTH-CARE KNOWLEDGE TO SUPPORT CLINICAL DECISIONS FOR CHRONICALLY ILL PATIENTS,” J. Biomed. Informat., vol. 45, no. 3, pp. 429–446, 2012.
EXPLANATION:
Chronically ill patients are complex health care cases that require the coordinated interaction of multiple professionals. A correct intervention of these sort of patients entails the accurate analysis of the conditions of each concrete patient and the adaptation of evidence-based standard intervention plans to these conditions. There are some other clinical circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases or prevention, whose detection depends on the capacities of deduction of the professionals involved. In this paper, we introduce ontology for the care of chronically ill patients and implement two personalization processes and a decision support tool. The first personalization process adapts the contents of the ontology to the particularities observed in the health-care record of a given concrete patient, automatically providing a personalized ontology containing only the clinical information that is relevant for health-care professionals to manage that patient. The second personalization process uses the personalized ontology of a patient to automatically transform intervention plans describing health-care general treatments into individual intervention plans. For comorbid patients, this process concludes with the semi-automatic integration of several individual plans into a single personalized plan. Finally, the ontology is also used as the knowledge base of a decision support tool that helps health-care professionals to detect anomalous circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases, or preventive actions. Seven health-care centers participating in the K4CARE project, together with the group SAGESA and the Local Health System in the town of Pollenza have served as the validation platform for these two processes and tool. Health-care professionals participating in the evaluation agree about the average quality 84% (5.9/7.0) and utility 90% (6.3/7.0) of the tools and also about the correct reasoning of the decision support tool, according to clinical standards.
AUTHOR AND PUBLICATION: I.Berges, J. Bermudez, and A. Illarramendi, “TOWARDS SEMANTIC INTEROPERABILITY OF ELECTRONIC HEALTH RECORDS,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 3, pp. 424–431, May 2012.
EXPLANATION:
Although the goal of
achieving semantic interoperability of electronic health records (EHRs) is
pursued by many researchers, it has not been accomplished yet. In this paper,
we present a proposal that smoothes out the way toward the achievement of that
goal. In particular, our study focuses on medical diagnoses statements. In
summary, the main contributions of our ontology-based proposal are the
following: first, it includes a canonical ontology whose EHR-related terms
focus on semantic aspects. As a result, their descriptions are independent of
languages and technology aspects used in different organizations to represent
EHRs. Moreover, those terms are related to their corresponding codes in
well-known medical terminologies. Second, it deals with modules that allow
obtaining rich ontological representations of EHR information managed by
proprietary models of health information systems. The features of one specific
module are shown as reference. Third, it considers the necessary mapping axioms
between ontological terms enhanced with so-called path mappings. This feature
smoothes out structural differences between heterogeneous EHR representations,
allowing proper alignment of information.
AUTHOR AND PUBLICATION: N. Lasierra,A.Alesanco, J.Garc´ıa, andD.O’Sullivan, “DATA MANAGEMENT IN HOME SCENARIOS USING AN AUTONOMIC ONTOLOGY-BASED APPROACH,” in Proc. of the 9th IEEE Int. Conf. Pervasive Workshop on Manag. Ubiquitous Commun. Services part of PerCom, 2012, pp. 94–99.
EXPLANATION:
An ontology-based approach to deal
with data and management procedure integration in home-based scenarios is
presented in this paper. The proposed ontology not only provides a means to
represent exchanged data but also to unify the way of accessing, controlling,
evaluating and transferring information remotely. The structure of this
ontology has been inspired by the autonomic computing paradigm, thus it
describes the tasks that comprise the MAPE (Monitor, Analyze, Plan and Execute)
process. Furthermore the use of SPARQL (Simple Protocol and RDF Query Language)
is proposed in this paper to express conditions and rules that determine the
performance of these tasks according to each situation. Finally two practical
application cases of the proposed ontology-based approach are presented.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
Telemonitoring systems face the problem of delivering medicine to the current growing population with chronic conditions while at the same time covering the dimensions of quality of care and new paradigms such as empowerment can be supported. By periodically collecting patients’ themselves clinical data (located at their home sites) and transferring them to physicians located in remote sites, patient’s health status supervision and feedback provision are possible.
This type of telemedicine system
guarantees patient control while reducing costs and avoiding hospital
overflows. These two sites (home site and healthcare site) comprised a typical
home-based telemonitoring system. At home site, data acquired by using MDs
together with the patient’s feedback are collected in a concentrator device
(HG) used to evaluate and/or transfer the acquired data outside the patient’s
home if necessary.
2.1.1 DISADVANTAGES:
2.2 PROPOSED SYSTEM:
We present an ontology-driven architecture to integrate data management and enable its communication in a telemonitoring scenario. It enables to not only integrate patient’s clinical data management but also technical data management of all devices that are included in the scenario. The proposed architecture includes two layers: the conceptual layer (the ontology) and the communication and data layer.
The conceptual layer uses the HOTMES and its extensions introduced specifically in the OWL-DL language was selected to define this ontology model. The second layer is based on WS technologies. WSs have been successfully used in network management and also in other works to exchange data modeled by ontology is our proposal, inspired on the representational state transfer (REST) style and based on a generic communication method, provides a different design approach that may be reusable for other systems based on ontologies.
Furthermore, security issues have been
considered. The aim is to define a flexible and scalable architecture in order
to address main challenges presented in home-based telemonitoring scenarios and
thus provide a means to integrate and transfer data supporting both clinical
and technical data management.
2.2.1 ADVANTAGES:
Ontologies provide a higher level of abstraction and have been successfully used in telemonitoring scenarios and other areas to provide knowledge representation and semantic integration, thus a common understanding about data exchanged by all the entities. Furthermore, its combination with rules allows providing personalized management services and thus personalized care.
We describe the details of an ontology approach in this domain, they do not devote much attention to the architecture implementation and the communication used to exchange the information described.
Our implementation of the ontology-based
system which may be of interest for the development of other ontology-based
applications in and outside the e-health domain the ontology for interpreting
the data transferred for the communication of end sources of the architecture.
The data and communication layer deals with data management and transmission.
2.3.1 HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data’s in the physical component is not identified.
Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.
MODELING RULES:
There are several common modeling rules when creating DFDs:
3.1 ARCHITECTURE DIAGRAM
3.2 DATAFLOW DIAGRAM
ADMIN:
USER:
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
3.3 SEQUENCE DIAGRAM:
3.5 ACTIVITY DIAGRAM:
CHAPTER 4
4.0 IMPLEMENTATION:
ONTOLOGIES:
According to one of the most widely accepted definitions of ontologies in computer science, ontology can be described as “an explicit and formal specification of a shared conceptualization”. In simple words, ontologies represent concepts and basic relationships for the purpose of comprehension of a common knowledge area. To develop an ontology means to formalize a common view of a certain domain.
1) OWL Language: In computer science, there are plenty of formal languages that can be used to define and constructontologies. These languages allow encoding knowledge contained in ontology in a simple and formal way. However, the standardized RDF and OWL have been gaining popularity in the semantic web world. Ontology can be formally described in OWL using following basic elements: 1) classes; 2) individuals; and 3) properties. These elements are used in order to describe concepts, instances, or members of a class and relationships between individuals of two classes (object properties) or to link individuals with datatype values, respectively (data type properties). Apart from these basic elements OWL provides with class descriptors used to precisely describe OWL classes which includes properties restrictions (value and cardinality constraints), class axioms, properties axioms, and properties over individuals.
2) Rules: Generally, ontology-based solutions combine knowledge presented in ontologies with dynamic knowledge presented by the use of rules. A system based on the use of rules usually contains a set of if-then rules (which indicate what should be done according to a situation) and a rule engine used to apply them. By using rules, the behavior of individuals can be expressed inside a domain. Hence, they can be used to generate new knowledge and can also be used to provide personalized services. One of the most popular languages for rules definition is SWRL.
However, in our study, we used SPARQL to define some rules is a query language it can be used as a rule language by combining CONSTRUCT clause and FILTER restrictions. On the one hand, the CONSTRUCT query form returns a single RDF graph built based on the results of matching with the graph pattern of the query and by taking the specified graph template. On the other hand, the FILTER clause can be used to restrict solutions to those which the filter expression considers as TRUE. Only if the filter function evaluates to true is the solution to be included in the solution sequence. Note that although this language was good enough for our purpose, its limitations should be studied for other purposes (e.g., recursive tasks) and the adequacy of SWRL could be studied for complex applications.
WEB SERVICES
Web services are used in this study as software technology to access and exchange information modeled by the ontology. According to the W3C, a WS is a “software system designed to support interoperable machine-to-machine interaction over a communication network”. Systems may interact with the web services by exchanging SOAP messages serialized in XML for its message format and sent over other application layer protocols, usually HTTP. Although SOAP-based web services are the most popular types of WSs, there are other styles of programming a WS such as the REST style.
1) Rest Style for DesigningWeb Services: REST is a style of software architecture for distributed hypermedia systems such as the World Wide Web first defined in 2000 by Fielding. This style is based on the idea of transferring the representations of resources, a resource being any item of interest. One of the key advantages of the REST architecture are scalability of components and generality of interfaces. Although REST was initially described in the context of HTTP, this paradigm can be applied to other protocols or implementations. Web services can also be described using this style. A WS implemented using HTTP and the principles of REST architecture is designated as REST(ful) WS. Requests made from the client and responses from the WS are used to transfer resources information. Each resource is identified through an URI. Stateless behavior of data using XML and/or JSON and explicitly used HTTP methods (PUT, GET, POST, DELETE) to exchange resources are the key characteristics of a REST(ful) WS.
4.1 MODULES:
MANAGEMENT PROFILE:
DATA AND COMMUNICATION LAYER:
HG AND TS MANAGEMENT MODULES:
COMMUNICATION FLOW AND WORKFLOW:
4.3 MODULE DESCRIPTION:
CLINICAL MANAGEMENT PROFILE:
COPD patients were identified as candidates to be monitored at home sites. From a clinical point of view, it was an interesting case study (some estimations suggest that up to 10% of the European population suffers COPD). From a technical point of view, the case of the COPD patient led to define a complex technical management profile (because different MDs are required to be used by the patient) and interesting option to test the performance of the agent. Hence, one patient profile was designed according to the clinical HOTMES ontology and one technical management profile was designed according to the technical HOTMES ontology.
The patient profile includes the required
tasks to monitor a COPD patient such as controlling the FEV1 measurement in
order to detect the presence and severity of the airway obstruction. It was
configured by a primary care physician by means of published clinical
guidelines in patient profile included 15 monitoring task, 11 analysis
task, 9 planning task, and 3 execution task. This configuration led to include
144 new instances and to configure 18 rules. The details of this profile and
its evaluation to configure other type of profiles can be technical
management profile was designed to monitor the state of theMDs used by the
COPD patient (weighing scale, a blood pressure monitor, a pulse-oximeter, and a
glucometer) and the consumption of resources of the correspondent HG. In
addition rules were configured and 83 new instances were required to be
configured in the technical management profile in additional information
of the application of the HOTMES ontology for technical tasks.
DATA AND COMMUNICATION LAYER:
In the data layer, the communication between the end sites is established using WS technologies. Consequently, a WS has been designed to be placed in the TS and also a web client to be installed in the HG (to establish a communication with the TS). This communication allows the HG to ask for its associated management profile to the TS and to transmit acquired information from the HG to the TS.
A REST WS was developed in order to enhance the scalability and flexibility of the architecture and improve the performance (efficiency). This WS comprises and defines a set of operations over the following resources: an OWL ontology, the rules (transferred by means of an XML), OWL individuals (sent by the IndividualWS structure), properties datatype values corresponding to an individual (identified by the URI of the individual and the URI of the property sent in a string generic type), and inform messages to provide some control functions to the web pair communication.
Each one of these resources was
identified by an URI, and a set of operations was defined for each particular
resource using HTTP methods (e.g., GET or PUT). This WS interface allows
information described in the ontology to be exchanged in a generic manner. This
is one key that contributes to the reusability and easy extension of the architecture.
Described communication methods do not depend on the knowledge itself described
in the ontology (related to the service) but on the fact of using an ontology
to represent such knowledge. A summary of the resources and defined operations is
depicted in Table I. As mentioned in the description of the converter module, individuals
are exchanged by using a developed structure designated as IndividualWS.
Using OWL language, an individual of the ontology can be described as a member
of a class with individual axioms or facts as individual property values
(datatype and object properties).
HG AND TS MANAGEMENT MODULES:
Two management modules and web technology modules inside the HG and the TS constitute the main parts of the telemedicine system (see Fig. 1). The modules that comprise the architecture have been developed using .NET technologies. Specifically, the .NET framework (version 3.5) has been used to process the ontology and create new instances, data acquisition, and manipulation when the rules are applied. Regarding the web modules the components of the remote management module installed in the TS are depicted in Fig. 1. This management module includes the following three components:
1) Ontology knowledge base module: This module contains the ontology knowledge models and the instances of the registered management profiles. The TDB triple-store has been used to store the ontology model and new instances in this knowledge base module.
2) Converter module: The communication module of this architecture is mainly based on OWL instances exchanged generically by means of a developed object structure named IndividualWS. The converter module is used to wrap and unwrap the individuals structure used to exchange information with web clients. Furthermore, this module incorporates some reasoning tasks. Ontology-based reasoning is used in order to check instances before including new information
in the model and to ensure the consistency of the model.
3)
Rules module: This module is used to store rules
associated with each management profile. These rules are subsequently transferred
by means of an XML file. As shown in Fig. 1, an additional GUI is required in
order to make easier for EM, technical or clinical (physician), the process of
defining the profiles and the rules. We are currentlyworking in the development
of this GUI combining ontology visualization techniques and usability methods.
The methodology used to design this interface components of the management
module installed in the HG are equally depicted in Fig. 1. This last management
module has been designated the “Semantic Autonomic Agent.” This module plays a
key role in the architecture. It is in charge of integrating incoming data and
executing the management tasks described in the management profile.
The communication between this agent and the management module installed at the remote site is established through a web client connection to the WS installed in the remote TS. The architecture of the agent comprises the ontology knowledge base module, the rules module, the converter module, and the following modules.
1) MAPE module: This module constitutes the computing core of the agent. It will be used to run the tasks specified in each management profile, hence to execute the closed loop from the MAPE loop process.
2) Integrator module: Information transferred by MDs and also contextual data provided by patients will be acquired in this module, which integrates data coming from different data sources.
3) Reminders and alarms module: This module includes clock functionalities to ask patients about data (reminders) or to collect information from a specific software resource.
4) Actions module: This last module is
used to execute actions described within the execution tasks of the management
profile if an abnormal finding occurs.
FLOW AND WORKFLOW PERFORMANCE:
All the modules and sources involved in the management procedure. The first step (see Fig. 3) consists in the download of the management profile (patient profile or technical profile). First of all, an instance of the management profile should be configured by an EM placed at a remote site. Furthermore, a set of individual rules should be configured for each particular management purpose. As shown in Fig. 3, the designed GUI helps the physician with the ontology instantiation process and the rules definition. The outputs of this interface (which uses selected classes of the ontology as a navigation tool) are a personalized management profile and a set of rules gathered in an XML file. Other functionalities such as queries over acquired data or crossing data among patients to take some decisions could be of interest to be included in this tool.
The communication is always initiated by the user (web client at HG). Through a connection to the web service, the user (the patient in the telemonitoring scenario) situated at home site will acquire the required management profile. As shown in Fig. 3, if the user requests for an update of his/her management profile, then the version of the available profile at the TS will be requested for its evaluation (GET property value). When the user requests a new management profile, first, it is checked whether the ontology to download it is available (GET ontology). After that, the rules and the management profile will be downloaded when required.
The methods involved are 1) GET (rules)
and 2) GET (individual). Note that the TLS authentication phase is not depicted
in Fig. 3, but it is initially carried out in order to allow the web client
connection to the web service. As depicted in Fig. 3, the associated management
profile is extracted from the ontology and the instances of the ontology
managed by Jena are wrapped into the IndividualWS structure through the
converter module. Once the management profile is in the HG, it will be
processed into the converter module, unwrapped, and inserted as individuals
managed by Jena in the ontology. Once the management profile has been
included in the ontology knowledge base module of the HG, it will be evaluated in
the MAPE module and the management procedure will be performed by running the
tasks specified in the profile.
CHAPTER 5
5.0 SYSTEM STUDY:
5.1 FEASIBILITY STUDY:
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
5.1.1 ECONOMICAL FEASIBILITY:
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
5.1.3 SOCIAL FEASIBILITY:
The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.
5.2 SYSTEM TESTING:
Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.
This creates two problems, the time lag
between the cause and the appearance of the problem and the effect of the
system errors on the files and records within the system. A small system error
can conceivably explode into a much larger Problem. Effective testing early in
the purpose translates directly into long term cost savings from a reduced
number of errors. Another reason for system testing is its utility, as a
user-oriented vehicle before implementation. The best programs are worthless if
it produces the correct outputs.
5.2.1 UNIT TESTING:
| Description | Expected result |
| Test for application window properties. | All the properties of the windows are to be properly aligned and displayed. |
| Test for mouse operations. | All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions. |
A program
represents the logical elements of a system. For a program to run
satisfactorily, it must compile and test data correctly and tie in properly
with other programs. Achieving an error free program is the responsibility of
the programmer. Program testing checks
for two types
of errors: syntax
and logical. Syntax error is a
program statement that violates one or more rules of the language in which it
is written. An improperly defined field dimension or omitted keywords are
common syntax errors. These errors are shown through error message generated by
the computer. For Logic errors the programmer must examine the output
carefully.
5.1.2 FUNCTIONAL TESTING:
Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem. The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.
| Description | Expected result |
| Test for all modules. | All peers should communicate in the group. |
| Test for various peer in a distributed network framework as it display all users available in the group. | The result after execution should give the accurate result. |
5.1. 3 NON-FUNCTIONAL TESTING:
The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:
5.1.4 LOAD TESTING:
An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.
| Description | Expected result |
| It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. | Should designate another active node as a Server. |
5.1.5 PERFORMANCE TESTING:
Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.
| Description | Expected result |
| This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management. | Should handle large input values, and produce accurate result in a expected time. |
5.1.6 RELIABILITY TESTING:
The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.
| Description | Expected result |
| This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. | In case of failure of the server an alternate server should take over the job. |
5.1.7 SECURITY TESTING:
Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.
| Description | Expected result |
| Checking that the user identification is authenticated. | In case failure it should not be connected in the framework. |
| Check whether group keys in a tree are shared by all peers. | The peers should know group key in the same group. |
5.1.8 WHITE BOX TESTING:
White box testing, sometimes called glass-box testing is a test case design method that uses the control structure of the procedural design to derive test cases. Using white box testing method, the software engineer can derive test cases. The White box testing focuses on the inner structure of the software structure to be tested.
| Description | Expected result |
| Exercise all logical decisions on their true and false sides. | All the logical decisions must be valid. |
| Execute all loops at their boundaries and within their operational bounds. | All the loops must be finite. |
| Exercise internal data structures to ensure their validity. | All the data structures must be valid. |
5.1.9 BLACK BOX TESTING:
Black box testing, also called behavioral testing, focuses on the functional requirements of the software. That is, black testing enables the software engineer to derive sets of input conditions that will fully exercise all functional requirements for a program. Black box testing is not alternative to white box techniques. Rather it is a complementary approach that is likely to uncover a different class of errors than white box methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.
| Description | Expected result |
| To check for incorrect or missing functions. | All the functions must be valid. |
| To check for interface errors. | The entire interface must function normally. |
| To check for errors in a data structures or external data base access. | The database updation and retrieval must be done. |
| To check for initialization and termination errors. | All the functions and data structures must be initialized properly and terminated normally. |
All
the above system testing strategies are carried out in as the development,
documentation and institutionalization of the proposed goals and related
policies is essential.
CHAPTER 6
6.0 SOFTWARE DESCRIPTION:
Java technology is both a programming language and a platform.
With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.
You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.
A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.
The Java platform has two components:
You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.
The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.
The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.
Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.
The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.
However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.
An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.
A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.
How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:
The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.
We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:
Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.
Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.
The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.
From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.
The advantages
of this scheme are so numerous that you are probably thinking there must be
some catch. The only disadvantage of ODBC is that it isn’t as efficient as
talking directly to the native database interface. ODBC has had many detractors
make the charge that it is too slow. Microsoft has always claimed that the
critical factor in performance is the quality of the driver software that is
used. In our humble opinion, this is true. The availability of good ODBC
drivers has improved a great deal recently. And anyway, the criticism about
performance is somewhat analogous to those who said that compilers would never
match the speed of pure assembly language. Maybe not, but the compiler (or
ODBC) gives you the opportunity to write cleaner programs, which means you
finish sooner. Meanwhile, computers get faster every year.
6.6 JDBC:
In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.
To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.
JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.
The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.
Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.
The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:
SQL Level API
The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.
SQL Conformance
SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.
JDBC must be implemental on top of common database interfaces
The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.
Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.
This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
Because more often than not, the usual SQL calls
used by the programmer are simple SELECT’s,
INSERT’s,
DELETE’s
and UPDATE’s,
these queries should be simple to perform with JDBC. However, more complex SQL
statements should also be possible.
Finally we decided to precede the implementation using Java Networking.
And for dynamically updating the cache table we go for MS Access database.
Java ha two things: a programming language and a platform.
Java is a high-level programming language that is all of the following
Simple Architecture-neutral
Object-oriented Portable
Distributed High-performance
Interpreted Multithreaded
Robust Dynamic Secure
Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.
Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.
UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.
TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.
Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.
Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
The 32 bit address is usually written as 4 integers separated by dots.
A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.
A socket is a data structure maintained by the system
to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor.
In fact, under Windows, this handle can be used with Read File and Write File
functions.
#include <sys/types.h>
#include <sys/socket.h>
int socket(int family, int type, int protocol);
Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two
processes wishing to communicate over a network create a socket each. These are
similar to two ends of a pipe – but the actual pipe does not yet exist.
6.8 JFREE CHART:
JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:
A consistent and well-documented API, supporting a wide range of chart types;
A flexible design that is easy to extend, and targets both server-side and client-side applications;
Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);
JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.
Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);
Creating an appropriate dataset interface (plus
default implementation), a rendered, and integrating this with the existing
XYPlot class in JFreeChart; Testing, documenting, testing some more,
documenting some more.
Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.
There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.
The property editor mechanism in JFreeChart only
handles a small subset of the properties that can be set for charts. Extend (or
reimplement) this mechanism to provide greater end-user control over the
appearance of the charts.
CHAPTER 7
APPENDIX
7.1 SAMPLE SOURCE CODE
7.2 SAMPLE OUTPUT
CHAPTER 8
8.1 CONCLUSION:
This study describes architecture to enable data integration and its management in an ontology-driven telemonitoring solution implemented in home-based scenarios. This is an innovative architecture that facilitates the integration of several management services at home sites using the same software engine. The architecture has been specifically studied to support both technical and clinical services in the telemonitoring scenario, thus avoiding installing additional software for technical purposes.
HOTMES ontology used at the conceptual layer to describe a management profile on the one hand, our ontology contributes to integrate data and its management offering benefits in terms of knowledge representation, workflow organization, and self-management capabilities to the system. Its combination with rules allows providing personalized services.
This application ontology could be in
future improved by introducing concepts from domain ontology. On the other hand,
the data and communication layer of the architecture, based on the REST WS, was
oriented to minimizing the consumption of resources and providing reusable key
ideas for future ontology-based architecture developments.
8.2 FUTURE ENHANCEMENT
This solution represents a further step toward the possibility of establishing more effective home-based telemonitoring systems and thus improving the remote care of patientswith chronic diseases. As it was reported in, good telemedicine implementations are developed after a process where the dynamic interaction among a combination of socio-technical and also clinical factors is optimized. It means that additional work should be done (e.g., to measure the interaction of the
patient–doctor using the system and also
the truthfulness of the system for a long period of time) before adopting this
solution in a real scenario its complete development, first, a concordance study
should be conducted in order to determine its clinical efficiency. Then, a
social impact study should be conducted in order to determine how the system
allowed improving patient’s quality of life. Regarding these last studies, the
results presented in evidence the benefits of telemonitoring systems while
linking their success to the usability design issues and features.
CHAPTER 9
9.1 REFERENCES
[1] I. Martinez et al., “Seamless integration of ISO/IEEE11073 personal health devices and ISO/EN13606 electronic health records into an endto- end interoperable solution,” Telemed. J. E. Health, vol. 16, no. 10, pp. 993–1004, 2010.
[2] M. Figueredo and J. Dias, “Service oriented architecture to support realtime implementation of artifact detection in critical care monitoring,” in Proc. IEEE. Annu. Int. Conf. Eng. Med. Biol. Soc., 2011, pp. 4925–4928.
[3] JD. Trigo, I. Mart´ınez, A. Alesanco, A. Kollmann, J. Escayola, D. Hayn, G. Schreier, and J. Garc´ıa, “An integrated healthcare information system for end-to-end standardized exchange and homogeneous management of digital ECG formats,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 518–529, Jul. 2012.
[4] F. Paganelli and D. Giuli, “An ontology-based system for context-aware and configurable services to support home-based continuous care,” IEEE Trans. Inform. Tech. Biomed., vol. 15, no. 2, pp. 324–333, 2011.
[5] D. Ria˜no, F. Real, J. A. L´opez-Vallverd´u, F. Campana, S. Ercolani, P. Mecocci, R. Annicchiarico, and C. Caltagirone, “An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients,” J. Biomed. Informat., vol. 45, no. 3, pp. 429–446, 2012.
[6] I. Berges, J. Bermudez, and A. Illarramendi, “Towards semantic interoperability of electronic health records,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 3, pp. 424–431, May 2012.
[7] G. Mulligan and D. Gracanin, “A comparison of SOAP and REST implementations of a service based interaction independence middleware framework,” in Proc. Winter Simul. Conf., 2009, pp. 1423–1432.
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