Augmenting clinical decision-making processes for primary care physiotherapists based on state-of-the-art artificial intelligence and deep learning techniques.

The objective of this project is to demonstrate how patient-related physiotherapy data that is collected and stored in a structured way, can be used for data-analysis by the use of deep learning, a part of a broader family of machine learning methods. Neural networks will be used to determine which therapeutic approach can be best used for what type of patient to increase physical activity, thereby demonstrating the feasibility of using data-analysis to develop effective therapeutic strategies in patients with cardiorespiratory and metabolic diseases. Demonstrating the feasibility of data gathering, storage and analysis in physiotherapy in internal diseases in a primary care setting will be a first and major step in developing data-driven therapy. The results of this project will facilitate and enable further research in the development of data-driven medicine in multiple diseases, as well as the development of data-monitoring and tele-coaching application in healthcare. The combined expertise of both research groups, in partnership with the Belgian Physiotherapy Association (Axxon), allows this consortium to take a head start in data-driven physiotherapy research and to become a pioneer in this field in Europe.

Keywords:ARTIFICIAL INTELLIGENCE, PHYSICAL ACTIVITY, MACHINE LEARNING, REHABILITATION TECHNOLOGY

Disciplines:Data mining, Machine learning and decision making, Physiotherapy