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ERC

MODEST Report Summary

Project ID: 647573
Funded under: H2020-EU.1.1.

Periodic Reporting for period 1 - MODEST (Mathematical Optimization for clinical DEcision Support and Training)

Reporting period: 2015-07-01 to 2016-12-31

Summary of the context and overall objectives of the project

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. We follow 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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The main focus of the first reporting period was on the establishment of cooperations with clinical partners, on the acquisition and processing of data, and on the development of mathematical models and algorithms. This relates to projects in oncology, where data concerning leucocyte counts during maintenance therapy of different kinds of leukemia were assessed, and to cardiology, where ECG data was analyzed. In addition to the cooperation that were already mentioned in the proposal three further promising cooperations with clinical partners were started. Data has been analyzed, showing a great potential for optimization approaches. We could already publish first results concerning the mathematical modeling of acute myeloid leukemia and the Wolffe-Parkinson- White Syndrome. These results can and will be the basis for future research.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

This is yet an early stage in the project and the wider implications are hard to judge. However, the preliminary results indicate that there is a huge potential to improve health care quality by means of optimization-driven decision support for clinical doctors.
Record Number: 198121 / Last updated on: 2017-05-17