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Marco-Ruiz L.,University of Tromsø | Pedrinaci C.,Open University Milton Keynes | Maldonado J.A.,Polytechnic University of Valencia | Maldonado J.A.,VeraTech for Health | And 3 more authors.
Journal of Biomedical Informatics | Year: 2016

Background The high costs involved in the development of Clinical Decision Support Systems (CDSS) make it necessary to share their functionality across different systems and organizations. Service Oriented Architectures (SOA) have been proposed to allow reusing CDSS by encapsulating them in a Web service. However, strong barriers in sharing CDS functionality are still present as a consequence of lack of expressiveness of services’ interfaces. Linked Services are the evolution of the Semantic Web Services paradigm to process Linked Data. They aim to provide semantic descriptions over SOA implementations to overcome the limitations derived from the syntactic nature of Web services technologies. Objective To facilitate the publication, discovery and interoperability of CDS services by evolving them into Linked Services that expose their interfaces as Linked Data. Materials and methods We developed methods and models to enhance CDS SOA as Linked Services that define a rich semantic layer based on machine interpretable ontologies that powers their interoperability and reuse. These ontologies provided unambiguous descriptions of CDS services properties to expose them to the Web of Data. Results We developed models compliant with Linked Data principles to create a semantic representation of the components that compose CDS services. To evaluate our approach we implemented a set of CDS Linked Services using a Web service definition ontology. The definitions of Web services were linked to the models developed in order to attach unambiguous semantics to the service components. All models were bound to SNOMED-CT and public ontologies (e.g. Dublin Core) in order to count on a lingua franca to explore them. Discovery and analysis of CDS services based on machine interpretable models was performed reasoning over the ontologies built. Discussion Linked Services can be used effectively to expose CDS services to the Web of Data by building on current CDS standards. This allows building shared Linked Knowledge Bases to provide machine interpretable semantics to the CDS service description alleviating the challenges on interoperability and reuse. Linked Services allow for building ‘digital libraries’ of distributed CDS services that can be hosted and maintained in different organizations. © 2016 Elsevier Inc.

Bosca D.,Institute Universitario Of Aplicaciones Of Las Tecnologias Of La Informacion municaciones Avanzadas | Bosca D.,Polytechnic University of Valencia | Moner D.,Institute Universitario Of Aplicaciones Of Las Tecnologias Of La Informacion municaciones Avanzadas | Maldonado J.A.,Institute Universitario Of Aplicaciones Of Las Tecnologias Of La Informacion municaciones Avanzadas | And 2 more authors.
Studies in Health Technology and Informatics | Year: 2015

Messaging standards, and specifically HL7 v2, are heavily used for the communication and interoperability of Health Information Systems. HL7 FHIR was created as an evolution of the messaging standards to achieve semantic interoperability. FHIR is somehow similar to other approaches like the dual model methodology as both are based on the precise modeling of clinical information. In this paper, we demonstrate how we can apply the dual model methodology to standards like FHIR. We show the usefulness of this approach for data transformation between FHIR and other specifications such as HL7 CDA, EN ISO 13606, and openEHR. We also discuss the advantages and disadvantages of defining archetypes over FHIR, and the consequences and outcomes of this approach. Finally, we exemplify this approach by creating a testing data server that supports both FHIR resources and archetypes. © 2015 European Federation for Medical Informatics (EFMI).

Fuster-Garcia E.,VeraTech for Health | Breso A.,Polytechnic University of Valencia | Martinez-Miranda J.,Polytechnic University of Valencia | Rosell-Ferrer J.,Polytechnic University of Catalonia | And 2 more authors.
Information Fusion | Year: 2015

Actigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients. © 2014 Elsevier B.V. All rights reserved.

Gonzalez-Ferrer A.,Hospital Clinico San Carlos | Peleg M.,Haifa University | Marcos M.,Jaume I University | Maldonado J.A.,Polytechnic University of Valencia | Maldonado J.A.,VeraTech for Health
Journal of Medical Systems | Year: 2016

Delivering patient-specific decision-support based on computer-interpretable guidelines (CIGs) requires mapping CIG clinical statements (data items, clinical recommendations) into patients’ data. This is most effectively done via intermediate data schemas, which enable querying the data according to the semantics of a shared standard intermediate schema. This study aims to evaluate the use of HL7 virtual medical record (vMR) and openEHR archetypes as intermediate schemas for capturing clinical statements from CIGs that are mappable to electronic health records (EHRs) containing patient data and patient-specific recommendations. Using qualitative research methods, we analyzed the encoding of ten representative clinical statements taken from two CIGs used in real decision-support systems into two health information models (openEHR archetypes and HL7 vMR instances) by four experienced informaticians. Discussion among the modelers about each case study example greatly increased our understanding of the capabilities of these standards, which we share in this educational paper. Differing in content and structure, the openEHR archetypes were found to contain a greater level of representational detail and structure while the vMR representations took fewer steps to complete. The use of openEHR in the encoding of CIG clinical statements could potentially facilitate applications other than decision-support, including intelligent data analysis and integration of additional properties of data items from existing EHRs. On the other hand, due to their smaller size and fewer details, the use of vMR potentially supports quicker mapping of EHR data into clinical statements. © 2016, Springer Science+Business Media New York.

Breso A.,Polytechnic University of Valencia | Martinez-Miranda J.,CICESE | Fuster-Garcia E.,Polytechnic University of Valencia | Fuster-Garcia E.,VeraTech for Health | And 3 more authors.
International Journal of Human Computer Studies | Year: 2016

Human Computer Interaction (HCI) is a research field which aims to improve the relationship between users and interactive computer systems. A main objective of this research area is to make the user experience more pleasant and efficient, minimizing the barrier between the users cognition of what they want to accomplish and the computers understanding of the users tasks, by means of user-friendly, useful and usable designs. A bad HCI design is one of the main reasons behind user rejection of computer-based applications, which in turn produces loss of productivity and economy in industrial environments. In the eHealth domain, user rejection of computer-based systems is a major barrier to exploiting the maximum benefit from those applications developed to support the treatment of diseases, and in the worst cases a poor design in these systems may cause deterioration in the clinical condition of the patient. Thus, a high level of personalisation of the system according to users needs is extremely important, making it easy to use and contributing to the systems efficacy, which in turn facilitates the empowerment of the target users. Ideally, the content offered through the interactive sessions in these applications should be continuously assessed and adapted to the changing condition of the patient. A good HCI design and development can improve the acceptance of these applications and contribute to promoting better adherence levels to the treatment, preventing the patient from further relapses. In this work, we present a mechanism to provide personalised and adaptive daily interactive sessions focused on the treatment of patients with Major Depression. These sessions are able to automatically adapt the content and length of the sessions to obtain personalised and varied sessions in order to encourage the continuous and long-term use of the system. The tailored adaptation of session content is supported by decision-making processes based on: (i) clinical requirements; (ii) the patient's historical data; and (iii) current responses from the patient. We have evaluated our system through two different methodologies: the first one performing a set of simulations producing different sessions from changing input conditions, in order to assess different levels of adaptability and variability of the session content offered by the system. The second evaluation process involved a set of patients who used the system for 14-28 days and answered a questionnaire to provide feedback about the perceived level of adaptability and variability produced by the system. The obtained results in both evaluations indicated good levels of adaptability and variability in the content of the sessions according to the input conditions. © 2015 Elsevier Ltd.

Bosca D.,Polytechnic University of Valencia | Maldonado J.A.,Polytechnic University of Valencia | Maldonado J.A.,VeraTech for Health | Moner D.,Polytechnic University of Valencia | Robles M.,Polytechnic University of Valencia
Journal of Biomedical Informatics | Year: 2015

Clinical information models are increasingly used to describe the contents of Electronic Health Records. Implementation guides are a common specification mechanism used to define such models. They contain, among other reference materials, all the constraints and rules that clinical information must obey. However, these implementation guides typically are oriented to human-readability, and thus cannot be processed by computers. As a consequence, they must be reinterpreted and transformed manually into an executable language such as Schematron or Object Constraint Language (OCL). This task can be difficult and error prone due to the big gap between both representations. The challenge is to develop a methodology for the specification of implementation guides in such a way that humans can read and understand easily and at the same time can be processed by computers. In this paper, we propose and describe a novel methodology that uses archetypes as basis for generation of implementation guides. We use archetypes to generate formal rules expressed in Natural Rule Language (NRL) and other reference materials usually included in implementation guides such as sample XML instances. We also generate Schematron rules from NRL rules to be used for the validation of data instances. We have implemented these methods in LinkEHR, an archetype editing platform, and exemplify our approach by generating NRL rules and implementation guides from EN ISO 13606, openEHR, and HL7 CDA archetypes. © 2015 Elsevier Inc.

Marco-Ruiz L.,University Hospital of North Norway | Marco-Ruiz L.,Health Science University | Moner D.,Polytechnic University of Valencia | Maldonado J.A.,Polytechnic University of Valencia | And 5 more authors.
International Journal of Medical Informatics | Year: 2015

Background: The reuse of data captured during health care delivery is essential to satisfy the demands of clinical research and clinical decision support systems. A main barrier for the reuse is the existence of legacy formats of data and the high granularity of it when stored in an electronic health record (EHR) system. Thus, we need mechanisms to standardize, aggregate, and query data concealed in the EHRs, to allow their reuse whenever they are needed. Objective: To create a data warehouse infrastructure using archetype-based technologies, standards and query languages to enable the interoperability needed for data reuse. Materials and methods: The work presented makes use of best of breed archetype-based data transformation and storage technologies to create a workflow for the modeling, extraction, transformation and load of EHR proprietary data into standardized data repositories. We converted legacy data and performed patient-centered aggregations via archetype-based transformations. Later, specific purpose aggregations were performed at a query level for particular use cases. Results: Laboratory test results of a population of 230,000 patients belonging to Troms and Finnmark counties in Norway requested between January 2013 and November 2014 have been standardized. Test records normalization has been performed by defining transformation and aggregation functions between the laboratory records and an archetype. These mappings were used to automatically generate open EHR compliant data. These data were loaded into an archetype-based data warehouse. Once loaded, we defined indicators linked to the data in the warehouse to monitor test activity of Salmonella and Pertussis using the archetype query language. Discussion: Archetype-based standards and technologies can be used to create a data warehouse environment that enables data from EHR systems to be reused in clinical research and decision support systems. With this approach, existing EHR data becomes available in a standardized and interoperable format, thus opening a world of possibilities toward semantic or concept-based reuse, query and communication of clinical data. © 2015 Elsevier Ireland Ltd.

PubMed | Polytechnic University of Valencia and VeraTech for Health
Type: | Journal: Studies in health technology and informatics | Year: 2016

The need to achieve high levels of semantic interoperability in the health domain is regarded as a crucial issue. Nowadays, one of the weaknesses when working in this direction is the lack of a coordinated use of information and terminological models to define the meaning and content of clinical data. IHTSDO is aware of this problem and has recently developed the SNOMED CT Expression Constraint Language to specify subsets of concepts. In this paper, we describe an implementation of an execution engine of this language. Our final objective is to allow advanced terminological binding between archetypes and SNOMED CT as a fundamental pillar to get semantically interoperable systems. The execution engine is available at

PubMed | VeraTech for Health
Type: | Journal: Methods in molecular biology (Clifton, N.J.) | Year: 2014

The actigraphy is a cost-effective method for assessing specific sleep disorders such as diagnosing insomnia, circadian rhythm disorders, or excessive sleepiness. Due to recent advances in wireless connectivity and motion activity sensors, the new actigraphy devices allow the non-intrusive and non-stigmatizing monitoring of outpatients for weeks or even months facilitating treatment outcome measure in daily life activities. This possibility has propitiated new studies suggesting the utility of actigraphy to monitor outpatients with mood disorders such as major depression, or patients with dementia. However, the full exploitation of data acquired during the monitoring period requires the use of automatic systems and techniques that allow the reduction of inherent complexity of the data, the extraction of most informative features, and the interpretability and decision-making. In this study we purpose a set of techniques for actigraphy patterns analysis for outpatient monitoring. These techniques include actigraphy signal pre-processing, quantification, nonlinear registration, feature extraction, detection of anomalies, and pattern visualization. In addition, techniques for daily actigraphy signals modelling and simulation are included to facilitate the development and test of new analysis techniques in controlled scenarios.

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