Resultado final
This report will identify the key components of iHELP and define the interfaces and interactions between them An internal report will be developed in M3 and will serve as a basis to kickoff the research activities of the project planned to start in M4
Personalised health modelling and predictions IThis report provides details of the mechanisms approaches techniques AI algorithms that are implemented to realise the creation of personalised health and risk prediction models
Big data platform and knowledge management system IThis report provides an overview of the LeanXcale big data platform as well as its application and extension scenarios that are developed in the iHELP project to support the specific needs of personalised healthcare domain
State of the art and requirements analysis IThis report will examine the SotA for the technologies involved in iHELP present possible future trends and analyse the identified both use case and technical requirements In M3 an internal deliverable will be provided in order to provide input to the initial conceptual model and architecture During the course of the project the technologies and requirements related to iHELP will continue being investigated in order to ensure that the objectives and innovations of the project are valid work is performed taking into account the latest SotA and developments fulfil the identified goals and requirements
iHELP DSS suite with visual analytics IThis report describes the different aspects of the iHELP DSS suite covering the details of the underlying architecture different componentsservices used technologies and how the different components can be composed to provide selfservice DSS capability to different stakeholders in the iHELP project
iHELP DSS suite with visual analytics IIIThis report describes the different aspects of the iHELP DSS suite, covering the details of the underlying architecture, different components/services, used technologies and how the different components can be composed to provide self-service DSS capability to different stakeholders in the iHELP project.
Pilot setup and implementation of digital trials IIThis document will describe the initial setup and execution details of the different digital trials carried out in the pilots
Coordination of pilot scenarios for personalised healthcare - early risk identification, prevention and intervention measures IIThis document will outline the design of the pilot scenarios, including a description of them, the scenario requirements, the interrelationships between different pilots, the relevant instruments and platforms that will be utilized and the use of iHELP technologies.
Secondary data capture and interoperability IThis report describes the mechanisms developed to capture and ingest secondary data eg from mobile and wearable platforms in the iHELP big data platform covering APIs gateways security protocols query mechanisms message brokers and also interoperability mechanisms that allow data from multiple sources to be seamlessly used by analytic applications
Model library implementation and recalibration of adaptive models IIThis report provides details on how the developed prediction, prevention and intervention models will be stored and continuously refined or recalibrated based on the feedback gathered from the AI algorithms and user-centric applications
Primary data capture and ingestion IIIThis report provides the details of the data capture and ingestion (into the big data iHELP platform) mechanisms, covering the APIs, connectors to historic data repositories and querying mechanisms used to extract data from existing cohorts etc
Social Analytics for the study of societal factors and policy makingThis report provides the details of the complex event processing engine developed in T5.4 to capture and analyse social and societal factors that can influence certain risk. The evaluation of the social and societal factors is also used to provide input to the DSS functionalities, which serve the needs of policy makers
Pilot setup and implementation of digital trials IThis document will describe the initial setup and execution details of the different digital trials carried out in the pilots
Conceptual model and reference architecture IThis report will identify the key components of iHELP and define the interfaces and interactions between them An internal report will be developed in M3 and will serve as a basis to kickoff the research activities of the project planned to start in M4
User Centric Design IIn addition to the functional and nonfunctional specifications the user centric design approaches need to be defined to ensure that the solutions are designed and delivered in a way that makes them interesting and useful for the target users This report will provide the principles and methodology that will be applied to design usercentric personalised healthcare solutions
Data Collection and Pilot Assessments IThis report will include the outcomes of the evaluation for the iHELP approaches and solutions and the benefits they provide in the fight again Pancreatic Cancer
Standardisation and Quality Assurance of Heterogenous Data IIIThis report will describe the measures adopted and developed in the project to ensure effective contributions towards standardisation and quality assurance of healthcare data. The deliverable will highlight the standards used for various purposes (e.g. for data management, data sharing etc) and the approaches/techniques implemented to make sure that the data remains withing the quality constraints while being used by different stakeholders and applications in the project
Delivery mechanisms for personalised healthcare and real-time feedback IIThis report describes the different user centric applications developed in the iHELP project to deliver the personalised healthcare solutions. The use of mobile, IoT and wearable technologies is elaborated as well as the design of personalised engagement strategies and software interfaces that are used to deliver the personalised recommendations and gather feedback
Techniques for early risk identification, predictions and assessment IThis report provides details of the AI algorithms and implementation techniques that are used to perform early identification as well as predictions of Pancreatic Cancer risks in individuals The deliverable reports on the establishment of data pipelines and the configuration of the AI algorithms in accordance with EC ethics report recommendations to identify hidden trends and patterns in existing data that provide reliable bases for making prediction and also performing assessment of identified risks before they are used for predictions
Big data platform and knowledge management system IIThis report provides an overview of the LeanXcale big data platform as well as its application and extension scenarios that are developed in the iHELP project to support the specific needs of personalised healthcare domain.
Big data platform and knowledge management system IIIThis report provides an overview of the LeanXcale big data platform as well as its application and extension scenarios that are developed in the iHELP project to support the specific needs of personalised healthcare domain.
Social Analytics for the study of societal factors and policy making IThis report provides the details of the complex event processing engine developed in T54 to capture and analyse social and societal factors that can influence certain risk The evaluation of the social and societal factors is also used to provide input to the DSS functionalities which serve the needs of policy makers
Monitoring, Alerting, Feedback and Evaluation Mechanisms IIThis deliverable provides an overview of the different mechanisms that are developed to deal with the (real-time) data gathered from the user-centric applications against the personalised risk predictions and consequent recommendations
Communication and Collaboration Plan and Activities IIThis series of deliverables will describe the dissemination and collaboration strategy and the activities followed during the reporting periods as well as the results from these activities. An internal plan will be developed in M6 to drive the initial dissemination activities of the project.
Standardisation, IPR management and Ethics in iHELP IThis report will detail all engagement with standardisation bodies or similar fora and the corresponding potential contributions
Data Modelling and Integrated Health Records: Design and open specification IIThis report provides the overview of the data modelling approaches adopted in the iHELP project. The document also provides design details and open specifications for the management of integrated health records in the HHR structures
Delivery mechanisms for personalised healthcare and real-time feedback IThis report describes the different user centric applications developed in the iHELP project to deliver the personalised healthcare solutions The use of mobile IoT and wearable technologies is elaborated as well as the design of personalised engagement strategies and software interfaces that are used to deliver the personalised recommendations and gather feedback
Initial Publication PackagePublication on the initial set of materials that will define and promote projects identity It will include the creation of a project logo a project factsheet a presentation providing a general description of iHELP projects official web site and templates for the official documents to be developed within the project
Functional and Non-Functional Specifications IIThis report will describe the functional and nonfunction specifications of the iHELP solutions Documented through active involvement of all partners this report will provide the implementation details for the relevant approachesmethodologies and technical solutions The specifications will guide the development activities throughout the project
Coordination of pilot scenarios for personalised healthcare - early risk identification, prevention and intervention measures IThis document will outline the design of the pilot scenarios including a description of them the scenario requirements the interrelationships between different pilots the relevant instruments and platforms that will be utilized and the use of iHELP technologies
Primary data capture and ingestion IIThis report provides the details of the data capture and ingestion (into the big data iHELP platform) mechanisms, covering the APIs, connectors to historic data repositories and querying mechanisms used to extract data from existing cohorts etc
User Centric Design IIIn addition to the functional and nonfunctional specifications the user centric design approaches need to be defined to ensure that the solutions are designed and delivered in a way that makes them interesting and useful for the target users This report will provide the principles and methodology that will be applied to design usercentric personalised healthcare solutions
Design of personalised prevention and intervention measures IIThis report provides the design details of personalised prevention and intervention measures that are designed in T5.2, based on the models developed in T4.2 and in accordance with EC ethics report recommendations. The deliverable provides information of the risk and the targeted measure that is designed to address the risk(s) in specific personalise profiles
Techniques for early risk identification, predictions and assessment IIThis report provides details of the AI algorithms and implementation techniques that are used to perform early identification as well as predictions of Pancreatic Cancer risks in individuals. The deliverable reports on the establishment of data pipelines and the configuration of the AI algorithms (in accordance with EC ethics report recommendations) to identify hidden trends and patterns in existing data that provide reliable bases for making prediction and also performing assessment of identified risks before they are used for predictions.
Communication and Collaboration Plan and Activities IThis series of deliverables will describe the dissemination and collaboration strategy and the activities followed during the reporting periods as well as the results from these activities An internal plan will be developed in M6 to drive the initial dissemination activities of the project
iHELP DSS suite with visual analytics IIThis report describes the different aspects of the iHELP DSS suite, covering the details of the underlying architecture, different components/services, used technologies and how the different components can be composed to provide self-service DSS capability to different stakeholders in the iHELP project.
Standardisation and Quality Assurance of Heterogenous Data IIThis report will describe the measures adopted and developed in the project to ensure effective contributions towards standardisation and quality assurance of healthcare data The deliverable will highlight the standards used for various purposes eg for data management data sharing etc and the approachestechniques implemented to make sure that the data remains withing the quality constraints while being used by different stakeholders and applications in the project
Design of personalised prevention and intervention measures IThis report provides the design details of personalised prevention and intervention measures that are designed in T52 based on the models developed in T42 and in accordance with EC ethics report recommendations The deliverable provides information of the risk and the targeted measure that is designed to address the risks in specific personalise profiles
Data Modelling and Integrated Health Records: Design and open specification IThis report provides the overview of the data modelling approaches adopted in the iHELP project The document also provides design details and open specifications for the management of integrated health records in the HHR structures
Secondary data capture and interoperability IIThis report describes the mechanisms developed to capture and ingest secondary data (e.g. from mobile and wearable platforms) in the iHELP big data platform; covering APIs, gateways, security protocols, query mechanisms, message brokers and also interoperability mechanisms that allow data from multiple sources to be seamlessly used by analytic applications
Primary data capture and ingestion IThis report provides the details of the data capture and ingestion into the big data iHELP platform mechanisms covering the APIs connectors to historic data repositories and querying mechanisms used to extract data from existing cohorts etc
Project Management HandbookDescribes the project management structure procedures for communication documentation deliverables review payments and cost statements procedures to control project progress and risk management
Social Analytics for the study of societal factors and policy making IIThis report provides the details of the complex event processing engine developed in T5.4 to capture and analyse social and societal factors that can influence certain risk. The evaluation of the social and societal factors is also used to provide input to the DSS functionalities, which serve the needs of policy makers
Personalised health modelling and predictions IIIThis report provides details of the mechanisms (approaches, techniques, AI algorithms) that are implemented to realise the creation of personalised health and risk prediction models.
Personalised health modelling and predictions IIThis report provides details of the mechanisms (approaches, techniques, AI algorithms) that are implemented to realise the creation of personalised health and risk prediction models.
Exploitation plan IIThis deliverable release the exploitation plans including the overall joint approach to the results exploitation as well as the partners` individual exploitation plans describing their intentions for exploitation of iHELP outcomes, detail their plans to achieve the targeted exploitation goals and report progress on planned actions.
State of the art and requirements analysis IIThis report will examine the SotA for the technologies involved in iHELP present possible future trends and analyse the identified both use case and technical requirements In M3 an internal deliverable will be provided in order to provide input to the initial conceptual model and architecture During the course of the project the technologies and requirements related to iHELP will continue being investigated in order to ensure that the objectives and innovations of the project are valid work is performed taking into account the latest SotA and developments fulfil the identified goals and requirements
Standardisation and Quality Assurance of Heterogenous Data IThis report will describe the measures adopted and developed in the project to ensure effective contributions towards standardisation and quality assurance of healthcare data The deliverable will highlight the standards used for various purposes eg for data management data sharing etc and the approachestechniques implemented to make sure that the data remains withing the quality constraints while being used by different stakeholders and applications in the project
State of the art and requirements analysis IIIThis report will examine the SotA for the technologies involved in iHELP present possible future trends and analyse the identified both use case and technical requirements In M3 an internal deliverable will be provided in order to provide input to the initial conceptual model and architecture During the course of the project the technologies and requirements related to iHELP will continue being investigated in order to ensure that the objectives and innovations of the project are valid work is performed taking into account the latest SotA and developments fulfil the identified goals and requirements
Exploitation plan IThis deliverable release the exploitation plans including the overall joint approach to the results exploitation as well as the partners individual exploitation plans describing their intentions for exploitation of iHELP outcomes detail their plans to achieve the targeted exploitation goals and report progress on planned actions
Monitoring, Alerting, Feedback and Evaluation Mechanisms IThis deliverable provides an overview of the different mechanisms that are developed to deal with the (real-time) data gathered from the user-centric applications against the personalised risk predictions and consequent recommendations
Model library implementation and recalibration of adaptive models IThis report provides details on how the developed prediction prevention and intervention models will be stored and continuously refined or recalibrated based on the feedback gathered from the AI algorithms and usercentric applications
Functional and Non-Functional Specifications IThis report will describe the functional and nonfunction specifications of the iHELP solutions Documented through active involvement of all partners this report will provide the implementation details for the relevant approachesmethodologies and technical solutions The specifications will guide the development activities throughout the project
iHELP will participate in the Pilot on Open Research Data in H2020 and will endeavour to offer open access to its scientific results reported in publications to the relevant scientific data and to data generated during the course of the project The plan will identify the best practices and specific standards for the generated data and assess their suitability for sharing and reuse in accordance with official guidelines
Publicaciones
Autores:
Dhar, Eshita, Umashankar Upadhyay, Yaoru Huang, Mohy Uddin, George Manias, Dimosthenis Kyriazis, Usman Wajid, Hamza AlShawaf, and Shabbir Syed Abdul
Publicado en:
Digital health, 2023, Página(s) 20552076231158022, ISSN 2055-2076
Editor:
Sage
DOI:
10.1177/20552076231158022
Autores:
Yaoru Huang; Umashankar Upadhyay; Eshita Dhar; Li-Jen Kuo; Shabbir Syed-Abdul
Publicado en:
Cancers, Edición 8, 2023, Página(s) 4437, ISSN 2072-6694
Editor:
Multidisciplinary Digital Publishing Institute (MDPI)
DOI:
10.3390/cancers14184437
Autores:
Te-Min Ke; Artitaya Lophatananon; Kenneth R. Muir
Publicado en:
Cancers, Edición 5, 2022, Página(s) 4991, ISSN 2072-6694
Editor:
Multidisciplinary Digital Publishing Institute (MDPI)
DOI:
10.3390/cancers14204991
Autores:
Eshita Dhar; Adama Ns Bah; Irene Alice Chicchi Giglioli; Silvia Quer; Luis Fernandez-Luque; Francisco J. Núñez-Benjumea; Shwetambara Malwade; Mohy Uddin; Umashankar Upadhyay; Shabbir Syed-Abdul
Publicado en:
Cancers, Edición 54, 2023, Página(s) 1775, ISSN 2072-6694
Editor:
Multidisciplinary Digital Publishing Institute (MDPI)
DOI:
10.3390/cancers15061775
Autores:
Sarah J. Kitson; Olivia Aurangzeb; Jawaria Parvaiz; Artitaya Lophatananon; Kenneth R. Muir; Emma J. Crosbie
Publicado en:
Cancer Prevention Research, Edición 42, 2022, Página(s) 605–621, ISSN 1940-6215
Editor:
American Association for Cancer Research
DOI:
10.1158/1940-6207.capr-22-0129
Autores:
Sarah J. Kitson; Emma J. Crosbie; D. Gareth Evans; Aritaya Lophatananon; Kenneth R. Muir; Darren Ashcroft; Evan Kontopantelis; Glen P. Martin
Publicado en:
BJOG: An International Journal of Obstetrics & Gynaecology, Edición 33, 2023, Página(s) 1-10, ISSN 1471-0528
Editor:
John Wiley & Sons Ltd.
DOI:
10.1111/1471-0528.17729
Autores:
Te-Min Ke, Artitaya Lophatananon, Kenneth R. Muir
Publicado en:
Biomedicines, 2023, Página(s) 3206, ISSN 2227-9059
Editor:
MDPI
DOI:
10.3390/biomedicines11123206
Autores:
Bernardo Pereira Cabral; Luiza Amara Maciel Braga; Shabbir Syed-Abdul; Fabio Batista Mota
Publicado en:
Current Oncology; Volume 30; Edición 3; Pages: 3432-3446, Edición 13, 2023, Página(s) 3432-3446, ISSN 1718-7729
Editor:
MDPI
DOI:
10.3390/curroncol30030260
Autores:
Panagiotis Karamolegkos; Argyro Mavrogiorgou; Athanasios Kiourtis; Dimosthenis Kyriazis
Publicado en:
Information, Edición 40, 2023, Página(s) 93, ISSN 2078-2489
Editor:
Multidisciplinary Digital Publishing Institute (MDPI)
DOI:
10.3390/info14020093
Autores:
Manias, G., Azqueta-Alzúaz, A., Damiani, A., Dhar, E., Kouremenou, E., Patino-Martínez, M., Savino, M., Shabbir, S.A. and Kyriazis, D.
Publicado en:
Caring is Sharing–Exploiting the Value in Data for Health and Innovation, 2023, Página(s) 153-154, ISSN 0926-9630
Editor:
IOS Press
DOI:
10.3233/shti230092
Autores:
George Manias; Harm op den Akker; Ainhoa Azqueta; Diego Burgos; Nikola Dino Capocchiano; Borja Llobell Crespo; Athanasios Dalianis; Andrea Damiani; Krasimir Filipov; Giorgos Giotis; Maritini Kalogerini; Rostislav Kostadinov; Pavlos Kranas; Dimosthenis Kyriazis; Artitaya Lophatananon; Shwetambara Malwade; George Marinos; Fabio Melillo; Vicent Moncho Mas; Kenneth Muir; Marzena Nieroda; Antonio De Ni
Publicado en:
2021 IEEE Symposium on Computers and Communications (ISCC), 2021, Página(s) 1-8
Editor:
IEEE
DOI:
10.5281/zenodo.7221328
Autores:
George Marinos; Chrysostomos Symvoulidis; Dimosthenis Kyriazis
Publicado en:
2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), 2021, Página(s) 1-4
Editor:
IEEE
DOI:
10.1109/BioSMART54244.2021.9677838
Autores:
Pavlos Kranas
Publicado en:
2021
Editor:
E.T.S. de Ingenieros Informáticos (UPM)
DOI:
10.20868/upm.thesis.69145
Autores:
Temin Ke, Artitaya Lophatananon, Kenneth Muir, Xinzhu Yu
Publicado en:
Cancer Research, 2022, Página(s) 2236, ISSN 0008-5472
Editor:
American Association on Cancer Research
DOI:
10.1158/1538-7445.am2022-2236
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