Descripción del proyecto
Mejorar la toma de decisiones clínicas mediante la optimización matemática
Los médicos se enfrentan a la responsabilidad constante de tomar decisiones críticas en su trabajo diario. Estas decisiones abarcan un amplio abanico de ámbitos, desde la programación de citas hasta la determinación de las dosis óptimas para los tratamientos de quimioterapia. La base de estas decisiones es una combinación de los conocimientos adquiridos y la experiencia del médico. Sin embargo, el problema está en que no es fácil transferir esa experiencia tan valiosa ni acceder a ella. En el proyecto MODEST, financiado por el Consejo Europeo de Investigación, se pretende proporcionar a los médicos un sistema integral que mejore su capacidad de decisión y facilite la evaluación. Además, el equipo del proyecto busca dotar a las personas menos experimentadas de las herramientas necesarias para tomar decisiones informadas con seguridad. Para ello, en el proyecto se emplean varios modelos matemáticos, algoritmos y datos médicos que contribuyen a mejorar la eficacia.
Objetivo
Physicians need to make many important decisions per day. One clinical example is the scheduling and dosage of chemotherapy treatments. A second example is the discrimination of atrial fibrillation from atypical atrial flutter, based on ECG data. Such important and complex decisions are usually based on expert knowledge, accumulated throughout the life of a physician and shaped by subjective (and sometimes unconscious) experience. It is not readily transferable and may be unavailable in rural areas. At the same time, the available imaging, laboratory, and basic clinical data is abundant and waits to be used. This data is not yet systematically integrated and often single data-points are used to make therapy decisions.
More and more clinical decision making tasks will be modeled in terms of mathematical relations.
I propose a systematic approach that supports and trains individual decision making. The developed ideas, mathematical models, and optimization algorithms will be generic and widely applicable in medicine and beyond, but also exploit specific structures, resulting in a patient- and circumstance-specific personalized medicine.
This allows, e.g. a physician to first simulate the impact of his decisions on a computer and to consider optimized solutions.
In the future, it will be the rare and unwanted exception that an important decision can not be backed up by consultation of a model-driven decision support system or based upon a systematic model-driven training.
MODEST has a mathematical core. It builds on a comprehensive, interdisciplinary work program, based on disciplinary expertise in mixed-integer optimal control and existing collaborations with medical and educational experts. It is both timely, given the increasing availability of data and the maturity of mathematical methods, models, and software; as well as high-impact, due to the large number of clinical areas that may benefit from optimization-based decision support and training tools.
Ámbito científico
CORDIS clasifica los proyectos con EuroSciVoc, una taxonomía plurilingüe de ámbitos científicos, mediante un proceso semiautomático basado en técnicas de procesamiento del lenguaje natural.
CORDIS clasifica los proyectos con EuroSciVoc, una taxonomía plurilingüe de ámbitos científicos, mediante un proceso semiautomático basado en técnicas de procesamiento del lenguaje natural.
- medical and health sciencesclinical medicinecardiologycardiovascular diseasescardiac arrhythmia
- medical and health scienceshealth sciencespersonalized medicine
- medical and health sciencesclinical medicineoncologyleukemia
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencesmathematicsapplied mathematicsmathematical model
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
39106 Magdeburg
Alemania