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Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation

Author

Listed:
  • Carlos Fernández-Llatas

    (Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain)

  • Teresa Meneu

    (Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain)

  • Vicente Traver

    (Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain)

  • José-Miguel Benedi

    (Instituto Tecnológico de Informática (ITI), Universitat Politècnica de Vaència, Camino de Vera S/N, Valencia 46022, Spain)

Abstract

Born in the early nineteen nineties, evidence-based medicine (EBM) is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized.

Suggested Citation

  • Carlos Fernández-Llatas & Teresa Meneu & Vicente Traver & José-Miguel Benedi, 2013. "Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation," IJERPH, MDPI, vol. 10(11), pages 1-12, October.
    • Handle: RePEc:gam:jijerp:v:10:y:2013:i:11:p:5671-5682:d:30042
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    Cited by:

    1. Antonio Martinez-Millana & Aroa Lizondo & Roberto Gatta & Salvador Vera & Vicente Traver Salcedo & Carlos Fernandez-Llatas, 2019. "Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process," IJERPH, MDPI, vol. 16(2), pages 1-14, January.
    2. Victor Galvez & Rene de la Fuente & Cesar Meneses & Luis Leiva & Gonzalo Fagalde & Valeria Herskovic & Ricardo Fuentes & Jorge Munoz-Gama & Marcos Sepúlveda, 2020. "Process-Oriented Instrument and Taxonomy for Teaching Surgical Procedures in Medical Training: The Ultrasound-Guided Insertion of Central Venous Catheter," IJERPH, MDPI, vol. 17(11), pages 1-15, May.
    3. Gema Ibanez-Sanchez & Carlos Fernandez-Llatas & Antonio Martinez-Millana & Angeles Celda & Jesus Mandingorra & Lucia Aparici-Tortajada & Zoe Valero-Ramon & Jorge Munoz-Gama & Marcos Sepúlveda & Eric R, 2019. "Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case," IJERPH, MDPI, vol. 16(10), pages 1-22, May.

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