iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records
Creators
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George Manias1
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Harm op den Akker2
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Ainhoa Azqueta3
- Diego Burgos3
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Nikola Dino Capocchiano4
- Borja Llobell Crespo5
- Athanasios Dalianis6
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Andrea Damiani4
- Krasimir Filipov7
- Giorgos Giotis6
- Maritini Kalogerini6
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Rostislav Kostadinov8
- Pavlos Kranas9
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Dimosthenis Kyriazis1
- Artitaya Lophatananon10
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Shwetambara Malwade11
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George Marinos1
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Fabio Melillo12
- Vicent Moncho Mas5
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Kenneth Muir10
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Marzena Nieroda10
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Antonio De Nigro12
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Claudia Pandolfo12
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Marta Patiño-Martinez3
- Florin Picioroaga13
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Aristodemos Pnevmatikakis2
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Shabbir Syed-Abdul11
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Tanja Tomson14
- Dilyana Vicheva8
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Usman Wajid15
- 1. University of Piraeus
- 2. Innovation Sprint
- 3. Universidad Politécnica de Madrid
- 4. Fondazione Policlinico Universitario Agostino Gemelli IRCCS
- 5. Hospital de Dénia-MarinaSalud
- 6. Athens Technology Centre
- 7. KODAR Systems
- 8. Medical University Plovdiv
- 9. LeanXcale
- 10. University of Manchester
- 11. Taipei Medical University
- 12. Engineering Ingegneria Informatica SpA
- 13. Siemens SRL
- 14. Karolinska Institutet
- 15. Information Catalyst for Enterprise
Description
Scientific and clinical research have advanced the ability of healthcare professionals to more precisely define diseases and classify patients into different groups based on their likelihood of responding to a given treatment, and on their future risks. However, a significant gap remains between the delivery of stratified healthcare and personalization. The latter implies solutions that seek to treat each citizen as a truly unique individual, as opposed to a member of a group with whom they share common risks or health-related characteristics. Personalisation also implies an approach that takes into account personal characteristics and conditions of individuals. This paper investigates how these desirable attributes can be developed and introduces a holistic environment, the iHELP, that incorporates big data management and Artificial Intelligence (AI) approaches to enable the realization of data-driven pathways where awareness, care and decision support is provided based on person-centric early risk prediction, prevention and intervention measures.
Files
ICTS4eHealth_iHelp_camera_ready_v1.0.pdf
Files
(450.4 kB)
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