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

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Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Assessment of AI-based decision support solutions offered by iHELP

This report benchmarks the AI-based analytic and DSS techniques against established KPIs to provide an assessment of the reliability, adaptability, robustness, efficiency and explainability of AI solutions

Conceptual model and reference architecture II

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 I

This 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 I

This 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 I

This 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 I

This 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 III

This 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.

Assessment of usability aspects of iHELP solutions

This report provides details of the usability studies carried out across different stakeholder groups (represented by partners) to highlight the usability aspects of the iHELP solutions and the user satisfaction rates achieved through the implementation of end user development and usability principles

Pilot setup and implementation of digital trials II

This document will describe the initial setup and execution details of the different digital trials carried out in the pilots

Assessment of socio-physical, human, and societal factors through iHELP solutions

This report describes the activities that are specifically designed to assess the social and societal impact of the iHELP solutions. The rise in awareness about health conditions, the support offered by different stakeholders in the healthcare domain and the influence of social and societal factors on the health of individuals (especially people who are either suffering from Cancer or are at risk of developing Cancer) will be elaborated in this report.

Coordination of pilot scenarios for personalised healthcare - early risk identification, prevention and intervention measures II

This 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 I

This 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 II

This 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 III

This 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 making

This 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 I

This document will describe the initial setup and execution details of the different digital trials carried out in the pilots

Conceptual model and reference architecture I

User Centric Design I

In 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 I

This 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 III

This 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 II

This 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 I

This 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 II

Technical assessment report

This report will provide a detailed account of assessment activities carried out to evaluate the impact of the iHELP technology solutions and their usefulness for different stakeholders in the healthcare domain. The report will provide an overview of the different benchmarking techniques used in the assessment process.

Big data platform and knowledge management system III

Techniques for early risk identification, predictions and assessment III

This 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.

Social Analytics for the study of societal factors and policy making I

This 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

Standardisation, IPR management and Ethics in iHELP II

This report will detail all engagement with standardisation bodies or similar fora and the corresponding potential contributions

Monitoring, Alerting, Feedback and Evaluation Mechanisms II

This 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 II

This 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 I

This 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 II

This 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 I

This 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 Package

Publication 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 II

This 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

Adoption roadmap

This report will provide a roadmap for adoption and exploitation of iHELP outcomes by different stakeholders in multidisciplinary cases. The roadmap wilt highlight current concerns and limitations in the personalised healthcare and policy making ecosystem and means to overcome them.

Coordination of pilot scenarios for personalised healthcare - early risk identification, prevention and intervention measures I

This 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 II

User Centric Design II

Design of personalised prevention and intervention measures II

This 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 II

This 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 I

This 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 II

Standardisation and Quality Assurance of Heterogenous Data II

This 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 I

This 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 I

This 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 II

This 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 I

This 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 Handbook

Describes 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 II

Exploitation plan III

This 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.

Personalised health modelling and predictions III

This 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 II

This 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 II

State of the art and requirements analysis II

Standardisation and Quality Assurance of Heterogenous Data I

This 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 III

Data Collection and Pilot Assessments II

This 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.

Exploitation plan I

This 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 I

Communication and Collaboration Plan and Activities III

Model library implementation and recalibration of adaptive models I

This 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 I

Data Management Plan

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

Publications

A scoping review to assess the effects of virtual reality in medical education and clinical care

Auteurs: Dhar, Eshita, Umashankar Upadhyay, Yaoru Huang, Mohy Uddin, George Manias, Dimosthenis Kyriazis, Usman Wajid, Hamza AlShawaf, and Shabbir Syed Abdul
Publié dans: Digital health, 2023, Page(s) 20552076231158022, ISSN 2055-2076
Éditeur: Sage
DOI: 10.1177/20552076231158022

Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records

Auteurs: George Manias, Ainhoa Azqueta-Alzúaz, Athanasios Dalianis, Jacob Griffiths, Maritini Kalogerini, Konstantina Kostopoulou, Eleftheria Kouremenou, Pavlos Kranas, Sofoklis Kyriazakos, Danae Lekka, Fabio Melillo, Marta Patiño-Martinez, Oscar Garcia-Perales, Aristodemos Pnevmatikakis, Salvador Garcia Torrens, Usman Wajid, Dimosthenis Kyriazis
Publié dans: Sensors, Numéro 24, 2024, Page(s) 1739, ISSN 1424-8220
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/s24061739

A Scoping Review to Assess Adherence to and Clinical Outcomes of Wearable Devices in the Cancer Population

Auteurs: Yaoru Huang; Umashankar Upadhyay; Eshita Dhar; Li-Jen Kuo; Shabbir Syed-Abdul
Publié dans: Cancers, Numéro 8, 2023, Page(s) 4437, ISSN 2072-6694
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers14184437

A Machine Learning Model to Predict Length of Stay and Mortality among Diabetes and Hypertension Inpatients

Auteurs: Diana Barsasella, Karamo Bah, Pratik Mishra, Mohy Uddin, Eshita Dhar, Dewi Lena Suryani, Dedi Setiadi, Imas Masturoh, Ida Sugiarti, Jitendra Jonnagaddala, Shabbir Syed-Abdul
Publié dans: Medicina, Numéro 58, 2024, Page(s) 1568, ISSN 1648-9144
Éditeur: MDPI
DOI: 10.3390/medicina58111568

Risk Factors Associated with Pancreatic Cancer in the UK Biobank Cohort

Auteurs: Te-Min Ke; Artitaya Lophatananon; Kenneth R. Muir
Publié dans: Cancers, Numéro 5, 2022, Page(s) 4991, ISSN 2072-6694
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers14204991

A Scoping Review and a Taxonomy to Assess the Impact of Mobile Apps on Cancer Care Management

Auteurs: 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
Publié dans: Cancers, Numéro 54, 2023, Page(s) 1775, ISSN 2072-6694
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/cancers15061775

Quantifying the Effect of Physical Activity on Endometrial Cancer Risk

Auteurs: Sarah J. Kitson; Olivia Aurangzeb; Jawaria Parvaiz; Artitaya Lophatananon; Kenneth R. Muir; Emma J. Crosbie
Publié dans: Cancer Prevention Research, Numéro 42, 2022, Page(s) 605–621, ISSN 1940-6215
Éditeur: American Association for Cancer Research
DOI: 10.1158/1940-6207.capr-22-0129

Predicting risk of endometrial cancer in asymptomatic women (PRECISION): Model development and external validation

Auteurs: Sarah J. Kitson; Emma J. Crosbie; D. Gareth Evans; Aritaya Lophatananon; Kenneth R. Muir; Darren Ashcroft; Evan Kontopantelis; Glen P. Martin
Publié dans: BJOG: An International Journal of Obstetrics & Gynaecology, Numéro 33, 2023, Page(s) 1-10, ISSN 1471-0528
Éditeur: John Wiley & Sons Ltd.
DOI: 10.1111/1471-0528.17729

An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank

Auteurs: Te-Min Ke, Artitaya Lophatananon, Kenneth R. Muir
Publié dans: Biomedicines, 2023, Page(s) 3206, ISSN 2227-9059
Éditeur: MDPI
DOI: 10.3390/biomedicines11123206

Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers

Auteurs: Bernardo Pereira Cabral; Luiza Amara Maciel Braga; Shabbir Syed-Abdul; Fabio Batista Mota
Publié dans: Current Oncology; Volume 30; Numéro 3; Pages: 3432-3446, Numéro 13, 2023, Page(s) 3432-3446, ISSN 1718-7729
Éditeur: MDPI
DOI: 10.3390/curroncol30030260

EverAnalyzer: A Self-Adjustable Big Data Management Platform Exploiting the Hadoop Ecosystem

Auteurs: Panagiotis Karamolegkos; Argyro Mavrogiorgou; Athanasios Kiourtis; Dimosthenis Kyriazis
Publié dans: Information, Numéro 40, 2023, Page(s) 93, ISSN 2078-2489
Éditeur: Multidisciplinary Digital Publishing Institute (MDPI)
DOI: 10.3390/info14020093

An Enhanced Standardization and Qualification Mechanism for Heterogeneous Healthcare Data

Auteurs: Manias, G., Azqueta-Alzúaz, A., Damiani, A., Dhar, E., Kouremenou, E., Patino-Martínez, M., Savino, M., Shabbir, S.A. and Kyriazis, D.
Publié dans: Caring is Sharing–Exploiting the Value in Data for Health and Innovation, 2023, Page(s) 153-154, ISSN 0926-9630
Éditeur: IOS Press
DOI: 10.3233/shti230092

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

Auteurs: 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
Publié dans: 2021 IEEE Symposium on Computers and Communications (ISCC), 2021, Page(s) 1-8
Éditeur: IEEE
DOI: 10.5281/zenodo.7221328

MICSurv: Medical Image Clustering for Survival risk group identification

Auteurs: George Marinos; Chrysostomos Symvoulidis; Dimosthenis Kyriazis
Publié dans: 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), 2021, Page(s) 1-4
Éditeur: IEEE
DOI: 10.1109/BioSMART54244.2021.9677838

Motor Consultas Analíticas Políglota

Auteurs: Pavlos Kranas
Publié dans: 2021
Éditeur: E.T.S. de Ingenieros Informáticos (UPM)
DOI: 10.20868/upm.thesis.69145

A novel integrated predictive model for pancreatic cancer

Auteurs: Temin Ke, Artitaya Lophatananon, Kenneth Muir, Xinzhu Yu
Publié dans: Cancer Research, 2022, Page(s) 2236, ISSN 0008-5472
Éditeur: American Association on Cancer Research
DOI: 10.1158/1538-7445.am2022-2236

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