16 | 12 | 2018

Supporting tele-health and AI-based clinical decision making with sensor data fusion and semantic interpretation: The USEFIL case study

Title: Supporting tele-health and AI-based clinical decision making with sensor data fusion and semantic interpretation: The USEFIL case study

Authors: Alexander Artikis, Panagiotis D. Bamidis, Antonis Billis, Charalampos Bratsas, Christos Frantzidis, Vangelis Karkaletsis, Manousos Klados, Evdokimos Konstantinidis, Stasinos Konstantopoulos,, Dimitris Kosmopoulos, Homer Papadopoulos, Stavros Peran tonis, Sergios Petridis, Constantine S. Spyropoulos

Abstract: In this paper we propose a three-layered architecture for clinical evidence-based Decision Support Systems. Our architecture allows for off-the-shelf, low cost sensors to be deployed in tele-health environments, using data fusion to achieve usable confidence in the sensor data. At the same time, the data fusion and semantic interpretation layer interfaces between sensor data and explicit rules encoding medical knowledge. This achieves a complete separation of the non-medical and medical knowledge, an important requirement for system adoption.