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Toro C.,Donostia International Physics Center | Sanchez E.,Donostia International Physics Center | Carrasco E.,Donostia International Physics Center | Mancilla-Amaya L.,University of Newcastle | And 7 more authors.
Cybernetics and Systems | Year: 2012

In this article we present an experience-based clinical decision support system (CDSS) for the diagnosis of Alzheimer's disease, which enables the discovery of new knowledge in the system and the generation of new rules that drive reasoning. In order to evolve an initial set of production rules given by medical experts we make use of the Set of Experience Knowledge Structure (SOEKS). An illustrative case of our system is also presented. Copyright © 2012 Taylor & Francis Group, LLC.

Sanchez E.,Vicomtech. IK | Toro C.,Vicomtech. IK | Carrasco E.,Vicomtech. IK | Bonachela P.,University of Seville | And 3 more authors.
2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, HEALTHCOM 2011 | Year: 2011

Alzheimer Disease (AD) has become a major issue in developed countries due to medical advances that have extended the population longevity. Recent advances in early detection date the initial stages of AD several years before the first recognizable symptoms appear visible. © 2011 IEEE.

Mesa I.,Centro de estudios e investigaciones técnicas de Gipuzkoa | Sanchez E.,Vicomtech IK4 | Sanchez E.,Biodonostia Health Research Institute | Sanchez E.,University of the Basque Country | And 14 more authors.
Cybernetics and Systems | Year: 2014

In this article we present the design and implementation of a mobile cardiac monitoring system oriented to patients in Phase II and III of cardiac rehabilitation. The complete monitoring system involves both hardware and software design perspectives. At the hardware level, we present a T-shirt with a 12-lead ECG system and an embedded inertial sensor for the monitoring of activity and energy expenditure. At the software level, a modular cloud platform performs data processing to detect relevant cardiac events and to provide advanced visualization capabilities. As a case study, we have implemented our system at the Cardiac Rehabilitation program at Donostia University Hospital (Spain). Finally, the validation of the 12-lead ECG recording system is also presented and discussed. © 2014 Copyright Taylor & Francis Group, LLC.

Sanchez E.,Vicomtech 4 Research Center | Toro C.,Vicomtech 4 Research Center | Carrasco E.,Vicomtech 4 Research Center | Bueno G.,University of Castilla - La Mancha | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Clinical Decision Support Systems (CDSS) are useful tools that aid physicians during different tasks such as diagnosis, treatment and patient monitoring. Multidisciplinary, heterogeneous and disperse clinical information and decision criteria have to be handled by CDSSs. For such tasks, Knowledge Engineering (KE) techniques and semantic technologies are very suitable, as they support (i) the integration of heterogeneous knowledge, (ii) the expression of rich and well-defined models for knowledge aggregation, and (iii) the application of logic reasoning for the generation of new knowledge. In this paper we propose a generic architecture of a CDSS based on semantic technologies, which also considers the reutilization and enhancement of former CDSS in an organization. Particularly, an implementation of the proposed architecture is also presented, aiming to support the early diagnosis of AD. © 2011 Springer-Verlag.

Sanchez E.,Vicomtech 4 Research Center | Sanchez E.,Biodonostia Health Research Institute | Toro C.,Vicomtech 4 Research Center | Artetxe A.,Vicomtech 4 Research Center | And 5 more authors.
Frontiers in Artificial Intelligence and Applications | Year: 2012

Clinical Decision Support Systems (CDSS) are computer applications that focus on assisting medical decisions required during clinical tasks. Although CDSS have been extensively studied for more than 30 years, their use is not broadly extended yet in daily clinical practice. Identified challenges of CDSS include (i) automating decision support, (ii) clinical workflow integration, (iii) ability of the system to be maintained and extended, (iv) timely advice and (v) evaluation of decisions effects and costs. In this paper, we hypothesize that Knowledge Engineering techniques and semantic technologies could be applied to CDSS in order to overcome the current identified challenges. We present a generic architecture for a Semantic CDSS, which we call SCDSS, and implementation guidelines for the breast cancer domain. Our approach follows a cyclic-federated paradigm allowing the reutilization of knowledge gathered at every stage of the clinical cycle. © 2012 The authors and IOS Press. All rights reserved.

Sanchez E.,Vicomtech 4 Research Center | Sanchez E.,Biodonostia Health Research Institute | Sanchez E.,University of the Basque Country | Toro C.,Vicomtech 4 Research Center | And 8 more authors.
Pattern Recognition Letters | Year: 2013

The integration of Clinical Decision Support Systems (CDSS) in nowadays clinical environments has not been fully achieved yet. Although numerous approaches and technologies have been proposed since 1960, there are still open gaps that need to be bridged. In this work we present advances from the established state of the art, overcoming some of the most notorious reported difficulties in: (i) automating CDSS, (ii) clinical workflow integration, (iii) maintainability and extensibility of the system, (iv) timely advice, (v) evaluation of the costs and effects of clinical decision support, and (vi) the need of architectures that allow the sharing and reusing of CDSS modules and services. In order to do so, we introduce a new clinical task model oriented to clinical workflow integration, which follows a federated approach. Our work makes use of the reported benefits of semantics in order to fully take advantage of the knowledge present in every stage of clinical tasks and the experience acquired by physicians. In order to introduce a feasible extension of classical CDSS, we present a generic architecture that permits a semantic enhancement, namely Semantic CDSS (S-CDSS). A case study of the proposed architecture in the domain of breast cancer is also presented, pointing some highlights of our methodology. © 2013 Elsevier B.V. All rights reserved.

Sanchez E.,Vicomtech 4 Research Center | Sanchez E.,Biodonostia Health Research Institute | Sanchez E.,University of the Basque Country | Peng W.,University of Newcastle | And 11 more authors.
Neurocomputing | Year: 2014

Clinical Decision Support Systems (CDSS) are active knowledge resources that use patient data to generate case specific advice. The fast pace of change of clinical knowledge imposes to CDSS the continuous update of the domain knowledge and decision criteria. Traditional approaches require costly tedious manual maintenance of the CDSS knowledge bases and repositories. Often, such an effort cannot be assumed by medical teams, hence maintenance is often faulty. In this paper, we propose a (semi-)automatic update process of the underlying knowledge bases and decision criteria of CDSS, following a learning paradigm based on previous experiences, such as the continuous learning that clinicians carry out in real life. In this process clinical decisional events are acquired and formalized inside the system by the use of the SOEKS and Decisional DNA experiential knowledge representation techniques. We propose three algorithms processing clinical experience to: (a) provide a weighting of the different decision criteria, (b) obtain their fine-tuning, and (c) achieve the formalization of new decision criteria. Finally, we present an implementation instance of a CDSS for the domain of breast cancer diagnosis and treatment. © 2014 Elsevier B.V.

Muro N.,Vicomtech IK4 | Muro N.,University of the Basque Country | Sanchez E.,Vicomtech IK4 | Sanchez E.,Biodonostia Health Research Institute | And 7 more authors.
Cybernetics and Systems | Year: 2016

Electronic Health Records are clinical information repositories that have been proposed primarily to provide access to all clinical data of a patient. They have been formally defined by a dual model composed of a reference model and an archetype model. Such dual approach allows semantic interoperability, thus making different systems understand each other. In this work we extend the current structure with a third Decisional Model that will allow reasoning over the embedded clinical contents. Such reasoning will be based on the reuse of the clinical experience gained by the corresponding clinical professionals during different decision procedures. Copyright © 2016 Taylor & Francis Group, LLC.

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