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 multimoda...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2194581 |
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| _version_ | 1850243744109953024 |
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| author | Mirjana Ivanovic Serge Autexier Miltiadis Kokkonidis Johannes Rust |
| author_facet | Mirjana Ivanovic Serge Autexier Miltiadis Kokkonidis Johannes Rust |
| author_sort | Mirjana Ivanovic |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b03a0f5b0c02470b904e632f6d4a0abb |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-b03a0f5b0c02470b904e632f6d4a0abb2025-08-20T01:59:56ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.21945812194581Quality medical data management within an open AI architecture – cancer patients caseMirjana Ivanovic0Serge Autexier1Miltiadis Kokkonidis2Johannes Rust3University of Novi Sad, Faculty of SciencesGerman Research Center for Artificial Intelligence (DFKI)NETCOMPANY-INTRASOFT S.A.German Research Center for Artificial Intelligence (DFKI)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.http://dx.doi.org/10.1080/09540091.2023.2194581quality of lifecancer patientsdata management in medical domainscloud/edge distributed environment |
| spellingShingle | Mirjana Ivanovic Serge Autexier Miltiadis Kokkonidis Johannes Rust Quality medical data management within an open AI architecture – cancer patients case Connection Science quality of life cancer patients data management in medical domains cloud/edge distributed environment |
| title | Quality medical data management within an open AI architecture – cancer patients case |
| title_full | Quality medical data management within an open AI architecture – cancer patients case |
| title_fullStr | Quality medical data management within an open AI architecture – cancer patients case |
| title_full_unstemmed | Quality medical data management within an open AI architecture – cancer patients case |
| title_short | Quality medical data management within an open AI architecture – cancer patients case |
| title_sort | quality medical data management within an open ai architecture cancer patients case |
| topic | quality of life cancer patients data management in medical domains cloud/edge distributed environment |
| url | http://dx.doi.org/10.1080/09540091.2023.2194581 |
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