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

Abstract

In contemporary society people constantly are 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 be properly prepared for intelligent processing and obtained results should be presented in a friendly way to the physicians/caregivers to recommend tailored actions that will improve patients’ quality of life. Advanced artificial intelligence approaches like machine/deep learning, federated learning, explainable artificial intelligence open new paths for more quality use of medical and health data in future. In this paper, we will focus on presentation of a part of a novel Open AI Architecture for cancer patients that is devoted to intelligent medical data management. Essential activities are data collection, proper design and preparation of data to be used for training machine learning predictive 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 a part of complex project in which 15 institutions from 8 European countries have been participated.

KEYWORDS:

• Quality of life
• cancer patients
• data management in medical domains
• Cloud/Edge distributed environment

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the ASCAPE project. The ASCAPE project has received funding from the European Union’s Horizon 2020 Research and Innovation Framework Programme under grant agreement No. 875351.