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Your search keyword '"Larburu, Nekane"' showing total 7 results
Start Over Author "Larburu, Nekane" Topic clinical practice guidelines
7 results on '"Larburu, Nekane"'

1. Real-World Evidence Inclusion in Guideline-Based Clinical Decision Support Systems: Breast Cancer Use Case

Author

Torres, Jordi, Alonso, Eduardo, Larburu, Nekane, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Juarez, Jose M., editor, Marcos, Mar, editor, Stiglic, Gregor, editor, and Tucker, Allan, editor

Published
2023
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2. Gathering Real World Evidence Through the Evaluation of Decision History.

Author

MURO, Naiara, LARBURU, Nekane, BOUAUD, Jacques, and SEROUSSI, Brigitte

Abstract

Clinical Practice Guidelines (CPGs) gather latest evidence-based results to guide and support clinicians over the decision-making process to provide best care. Nevertheless, clinical cases may be subject to some biases (understood as noncompliance with CPGs) that can lead to adapt care delivery. In this work an experience-based decision support leaning on the structuration of the Decisional Event concept for tracking and storing each clinical decision is presented. Moreover, a visual analytics tool is provided in order to facilitate the visualization of biases from guideline-based decision support and the identification and inclusion of realworld evidence into the reasoning process by augmenting the knowledge formalized in the implemented guidelines. [ABSTRACT FROM AUTHOR]

Published
2019
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3. Implementing Guideline-Based, Experience-Based, and Case-Based Approaches to Enrich Decision Support for the Management of Breast Cancer Patients in the DESIREE Project.

Author

SEROUSSI, Brigitte, LAMY, Jean-Baptiste, MURO, Naiara, LARBURU, Nekane, SEKAR, Booma Devi, GUÉZENNEC, Gilles, and BOUAUD, Jacques

Abstract

DESIREE is a European-funded project to improve the management of primary breast cancer. We have developed three decision support systems (DSSs), a guideline-based, an experience-based, and a case-based DSSs, resp. GL-DSS, EXP-DSS, and CB-DSS, that operate simultaneously to offer an enriched multimodal decision support to clinicians. A breast cancer knowledge model has been built to describe within a common ontology the data model and the terminoontological knowledge used for representing breast cancer patient cases. It allows for rule-based and subsumption-based reasoning in the GL-DSS to provide best patient-centered reconciled care plans. It also allows for using semantic similarity in the retrieval algorithm implemented in the CB-DSS. Rainbow boxes are used to display patient cases similar to a given query patient. This innovative visualization technique translates the question of deciding the most appropriate treatment into a question of deciding the colour dominance among boxes. [ABSTRACT FROM AUTHOR]

Published
2018
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4. Weighting Experience-Based Decision Support on the Basis of Clinical Outcomes' Assessment.

Author

MURO, Naiara, LARBURU, Nekane, BOUAUD, Jacques, and SEROUSSI, Brigitte

Abstract

Technologies such as decision support systems are expected to help clinicians implement clinical practice guidelines (CPGs) with the aim of decreasing practice variations and improving clinical outcomes. However, if CPGs provide recommendations to improve patient care, they may fail to take into account actual clinical outcomes associated to the recommended treatment, such as adverse events or secondary effects. In this paper, we present a novel experiencebased decision support approach applied to the management of breast cancer, the most commonly diagnosed cancer among women worldwide. Capitalizing on the clinical know-how of physicians and the modeling of patient's outcomes and toxicities in a computer interpretable way, we are able to discover new knowledge that helps improving patient-centered clinical care. This work is conducted within the EU Horizon 2020 project DESIREE. [ABSTRACT FROM AUTHOR]

Published
2017
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5. Architecture for a Multimodal and Domain-Independent Clinical Decision Support System Software Development Kit

Author

Muro, Naiara, Larburu, Nekane, Torres, Jordi, Kerexetab, Jon, Artolab, Garazi, Arrueb, Mónica, Macía, Iván, and Séroussi, Brigitte

Subjects
Computer Interpretable Guidelines, Clinical Decision Support System, Ontology, Decision Trees, Breast Cancer, Clinical Practice Guidelines, Decisional Event, Patient Outcomes, 3. Good health
Abstract

Digitalization of the decision-making process in healthcare has been promoted to improve clinical performance and patient outcomes. The implementation of Clinical Practice Guidelines (CPGs) using Clinical Decision Support Systems (CDSSs) is widely developed in order to achieve this purpose within clinical information systems. Nevertheless, due to several factors such as (i) incompleteness of CPG clinical knowledge, (ii) out-of-date contents, or (iii) knowledge gaps for specific clinical situations, guideline-based CDSSs may not completely satisfy clinical needs. The proposed architecture aims to cope with guideline knowledge gaps and pitfalls by harmonizing different modalities of decision support (i.e. guideline-based CDSSs, experience-based CDSSs, and data mining-based CDSSs) and information sources (i.e. CPGs and patient data) to provide the most complete, personalized, and up-to-date propositions to manage patients. We have developed a decisional event structure to retrieve all the information related to the decision-making process. This structure allows the tracking, computation, and evaluation of all the decisions made over time based on patient clinical outcomes. Finally, different user-friendly and easy-to-use authoring tools have been implemented within the proposed architecture to integrate the role of clinicians in the whole process of knowledge generation and validation. A use case based on Breast Cancer management is presented to illustrate the performance of the implemented architecture.

6. Architecture for a Multimodal and Domain-Independent Clinical Decision Support System Software Development Kit

Author

Muro, Naiara, Larburu, Nekane, Torres, Jordi, Kerexetab, Jon, Artolab, Garazi, Arrueb, Mónica, Macía, Iván, and Séroussi, Brigitte

Subjects
Computer Interpretable Guidelines, Clinical Decision Support System, Ontology, Decision Trees, Breast Cancer, Clinical Practice Guidelines, Decisional Event, Patient Outcomes, 3. Good health
Abstract

Digitalization of the decision-making process in healthcare has been promoted to improve clinical performance and patient outcomes. The implementation of Clinical Practice Guidelines (CPGs) using Clinical Decision Support Systems (CDSSs) is widely developed in order to achieve this purpose within clinical information systems. Nevertheless, due to several factors such as (i) incompleteness of CPG clinical knowledge, (ii) out-of-date contents, or (iii) knowledge gaps for specific clinical situations, guideline-based CDSSs may not completely satisfy clinical needs. The proposed architecture aims to cope with guideline knowledge gaps and pitfalls by harmonizing different modalities of decision support (i.e. guideline-based CDSSs, experience-based CDSSs, and data mining-based CDSSs) and information sources (i.e. CPGs and patient data) to provide the most complete, personalized, and up-to-date propositions to manage patients. We have developed a decisional event structure to retrieve all the information related to the decision-making process. This structure allows the tracking, computation, and evaluation of all the decisions made over time based on patient clinical outcomes. Finally, different user-friendly and easy-to-use authoring tools have been implemented within the proposed architecture to integrate the role of clinicians in the whole process of knowledge generation and validation. A use case based on Breast Cancer management is presented to illustrate the performance of the implemented architecture.

7. Une plateforme multimodale d'aide à la décision: Application à la prise en charge du cancer du sein dans le cadre du projet DESIREE.

Author

SEROUSSI, Brigitte, LAMY, Jean-Baptiste, PREBET, Coralie, NGO, Charlotte, TEIXEIRA, Luis, UGARRIZA, Amaia, SEKAR, Booma Devi, LARBURU, Nekane, MURO, Naiara, GUÉZENNEC, Gilles, and BOUAUD, Jacques

Subjects
*DECISION support systems, *MEDICAL quality control, *CASE-based reasoning, *CONCEPTUAL structures, *BREAST cancer, *ONTOLOGIES (Information retrieval)
Abstract

In order to improve health care quality, we have developed a platform including three clinical decision support systems (CDSSs) based on complementary approaches, (i) a CDSS based on clinical practice guidelines (CPGs) that provides patient-centered state of the art recommendations, (ii) a CDSS based on the experience gained when informed clinician users decide not to follow CPGs and make non-compliant decisions that they are asked to justify, and (iii) a case-based reasoning CDSS. The three CDSSs are smoothly interacting under the control of a domain ontology. Used as a conceptual and terminological structure, the domain ontology proposes a generic data model (entity-attribute-value) and a knowledge model used for ontological reasoning (subsumption) and deductive decision support (inferences). The platform has been developed to improve the management of breast cancer patients within the European Commission funded project DESIREE. [ABSTRACT FROM AUTHOR]

Published
2018
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