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

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...

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Bibliographic Details
Published inConnection science Vol. 35; no. 1
Main Authors Ivanovic, Mirjana, Autexier, Serge, Kokkonidis, Miltiadis, Rust, Johannes
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 31.12.2023
Taylor & Francis Ltd
Subjects
Artificial intelligence
Cancer
cancer patients
Cloud/Edge distributed environment
Data collection
Data management
data management in medical domains
Decision support systems
Deep learning
Explainable artificial intelligence
Machine learning
Patients
Prediction models
Quality of life
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Summary: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.
ISSN:0954-0091
1360-0494
DOI:10.1080/09540091.2023.2194581