iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records

被引：5

作者：

Manias, George ^{[1
]}

Op Den Akker, Harm ^{[2
]}

Azqueta, Ainhoa ^{[3
]}

Burgos, Diego ^{[3
,9
]}

Capocchiano, Nikola Dino ^{[4
]}

Crespo, Borja Llobell ^{[5
]}

Dalianis, Athanasios ^{[6
]}

Damiani, Andrea ^{[4
]}

Filipov, Krasimir ^{[7
]}

Giotis, Giorgos ^{[6
]}

Kalogerini, Maritini ^{[6
]}

Kostadinov, Rostislav ^{[8
]}

Kranas, Pavlos ^{[3
,9
]}

Kyriazis, Dimosthenis ^{[1
]}

Lophatananon, Artitaya ^{[10
]}

Malwade, Shwetambara ^{[11
]}

Marinos, George ^{[1
]}

Melillo, Fabio ^{[12
]}

Mas, Vicent Moncho ^{[5
]}

Muir, Kenneth ^{[10
]}

Nieroda, Marzena ^{[10
]}

De Nigro, Antonio ^{[12
]}

Pandolfo, Claudia ^{[12
]}

Patino-Martinez, Marta ^{[3
]}

Picioroaga, Florin ^{[13
]}

Pnevmatikakis, Aristodemos ^{[2
]}

Syed-Abdul, Shabbir ^{[11
]}

Tomson, Tanja ^{[14
]}

Vicheva, Dilyana ^{[8
]}

Wajid, Usman ^{[15
]}

机构：

[1] Univ Piraeus, Piraeus, Greece

[2] Innovat Sprint, Brussels, Belgium

[3] Univ Politecn Madrid, Madrid, Spain

[4] Fdn Policlin Univ Agostino Gemelli IRCCS, Rome, Italy

[5] Hosp Denia MarinaSalud, Alicante, Spain

[6] Athens Technol Ctr, Athens, Greece

[7] KODAR Syst, Plovdiv, Bulgaria

[8] Med Univ Plovdiv, Plovdiv, Bulgaria

[9] LeanXcale, Madrid, Spain

[10] Univ Manchester, Manchester, Lancs, England

[11] Taipei Med Univ, Taipei, Taiwan

[12] Engn Ingn Informat SpA, Rome, Italy

[13] Siemens SRL, Brasov, Romania

[14] Karolinska Inst, Solna, Sweden

[15] Informat Catalyst Enterprise, Northwich, England

来源：

26TH IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (IEEE ISCC 2021) | 2021年

关键词：

Holistic Health Records (HHRs); Pancreatic Cancer; Artificial Intelligence; PANCREATIC-CANCER; BEHAVIOR; RISK; EPIDEMIOLOGY; CHOICE; TOOLS; CARE;

D O I：

10.1109/ISCC53001.2021.9631475

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：8

共 44 条

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