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Quality medical data management within an open AI architecture – cancer patients case

Mirjana Ivanovic; Serge Autexier; Miltiadis Kokkonidis; Johannes Rust
In: Connection Science, Vol. 35, No. 1, Page 2194581, Taylor & Francis, 4/2023.


In contemporary society peopleconstantlyare facing situations that influence appearance of serious diseases. For the development of intelligent decision support systems and services in medical and health domains, it is necessary to collect huge amount of patients' complex data. Patient's multimodal data must b eproperly prepared for intelligent processing and obtained results should be presented in afriendly way to thephysicians/caregivers to recommendtailored actions that will improve patients' quality of life. Advanced artifi- cial intelligence approaches like machine/deep learning, federated learning, explainable artificial intelligenceopen new paths for more quality use of medical and health data in future. In this paper, we will focus on presentation of a p a r tof a novel Open A lArchitecture for cancer patients that is devotedtointelligent medicaldata man- agement. Essential activities are data collection, p r o p e rdesign and preparation of data to be used for training machine learningpre- dictive models. Another key aspect is oriented towards intelligent interpretation and visualisation of results about patient's quality of life obtained from machine learning models. The Architecture has been developed as ap a r t o fcomplex project in which 15 institutions from 8 European countries have been participated.


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