Project description
Enhancing clinical decision-making through mathematical optimisation
Physicians face the constant responsibility of making critical decisions in their daily work. These decisions encompass a wide range of areas, from scheduling appointments to determining optimal dosages for chemotherapy treatments. The basis for these decisions is a combination of the physician’s acquired knowledge and experience. However, the challenge lies in the fact that such valuable experience is not readily transferable or easily accessible. The ERC-funded MODEST project aims to provide physicians with a comprehensive system that enhances their decision-making abilities and facilitates assessment. Moreover, the project strives to equip less experienced individuals with the necessary tools to make informed decisions safely. To achieve this, the project employs a range of mathematical models, algorithms and medical data, which contribute to improved efficiency.
Objective
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.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- 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
Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
39106 Magdeburg
Germany