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Authors: Luigi Lella 1 ; Ignazio Licata 2 ; Gianfranco Minati 3 ; Christian Pristipino 4 ; Antonio De Belvis 5 and Roberta Pastorino 5

Affiliations: 1 ASUR, Regional Health Agency of Marche, AN and Italy ; 2 ISEM, Inst. for Scientific Methodology, PA and Italy ; 3 AIRS, Italian Association for Systems Research, MI and Italy ; 4 ASSIMSS, Italian Association for Systems Medicine & Healthcare, Rome and Italy ; 5 Section of Hygiene, Inst. of Public Health, Università Cattolica del Sacro Cuore, Rome and Italy

ISBN: 978-989-758-353-7

Keyword(s): e-Health, e-Health Applications, Pattern Recognition and Machine Learning, Decision Support Systems.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Cloud Computing ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; e-Health ; Health Engineering and Technology Applications ; Health Information Systems ; Knowledge-Based Systems ; Pattern Recognition and Machine Learning ; Platforms and Applications ; Symbolic Systems

Abstract: Innovative information systems which enable personalized medicine are presented. The designed decision support systems are expected to infer with an excellent level of accuracy the outcome of a therapeutic intervention through the analysis of biometric, genetic and environmental data. They are also capable to motivate their predictions according to a dynamic knowledge base, which is kept updated with new analysed cases. These systems can be used by researchers to identify useful correlations between biometric, genetic and environmental data with potential risks and benefits of certain therapeutic choices. They can also be used by the patients to choose the most appropriate therapeutic intervention according to their needs and expectations. In other words the presented decision support tools can realize the vision of the predictive, preventive, personalized and participatory (P4) medicine pursued by the systemic medicine.

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Paper citation in several formats:
Lella, L.; Licata, I.; Minati, G.; Pristipino, C.; De Belvis, A. and Pastorino, R. (2019). Predictive AI Models for the Personalized Medicine.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF, ISBN 978-989-758-353-7, pages 396-401. DOI: 10.5220/0007472203960401

@conference{healthinf19,
author={Luigi Lella. and Ignazio Licata. and Gianfranco Minati. and Christian Pristipino. and Antonio Giulio De Belvis. and Roberta Pastorino.},
title={Predictive AI Models for the Personalized Medicine},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF,},
year={2019},
pages={396-401},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007472203960401},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF,
TI - Predictive AI Models for the Personalized Medicine
SN - 978-989-758-353-7
AU - Lella, L.
AU - Licata, I.
AU - Minati, G.
AU - Pristipino, C.
AU - De Belvis, A.
AU - Pastorino, R.
PY - 2019
SP - 396
EP - 401
DO - 10.5220/0007472203960401

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