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CORDIS - EU research results
CORDIS

Privacy compliant health data as a service for AI development

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

PHASE IV AI Initial Architecture (opens in new window)

A description of the initial technical architecture.

Study initiation package – UC1-UC3 (opens in new window)

A compiled version of the mandatory deliverable for the retrospective, registry-based studies in PHASE IV AI.

User stories, usage scenarios and use case validation v2 (opens in new window)

User perspectives on PHASE IV AI use cases.

Dissemination, Communication and Exploitation Activities v1 (opens in new window)

Report on the project's dissemination, communication and exploitation activities.

User stories, usage scenarios and use case validation v1 (opens in new window)

User perspectives on PHASE IV AI use cases.

Legal and ethical framework and requirements v1 (opens in new window)

An overview of legal and ethical requirements for the PHASE IV AI technologies.

Dissemination, Communication and Exploitation Plan (opens in new window)

PHASE IV AI Dissemination, Communication and Exploitation Plan. Some sections of the exploitation plan could be considered sensitive (SEN).

PHASE IV AI Initial Technology Assessment and List (opens in new window)

A document reflecting the developed PHASE IV AI technologies to the original requirements and specifications.

Security and Privacy measures (opens in new window)

Description of the PHASE IV AI approaches to security anf privacy (technical measures).

Data Specifications (opens in new window)

Data Specifications based on the selected use cases.

Guideline on EHDS Alignment v1 (opens in new window)

Guidelines for aligning the PHASE IV AI technologies with the EHDS developments.

Data Harmonization for DaaS, MaaS and Specifications plan (opens in new window)

The deliverable will identify the heterogeneities of the data arising from the different medical imaging and labelling expert that exists in the data of the same data owner and the heterogeneities among different data owners arising from different EMRs, language and logging process etc. Following this, it will setup the roadmap for the documentation of both processes (OMOP CDM, semantic mismatches, metadata of demographic characteristics) indicating the right harmonisation tools and methodologies to prepare the data in a common and standardized data model within the health data hub.

Project Handbook, Quality Plan & Risk Management (opens in new window)

The project handbook prepared by the coordinator will guide the project's implementation.

Technologies for de-identification and synthetic data generation v1 (opens in new window)

Algorithms for advanced anonymization and synthetic data generation. Lead beneficiaries UTU (de-identification) and EUT (synthetic data generation).

DaaS Toolbox v1 (opens in new window)

ML algorithms for Data as a Service concept.Type: R+OTHER

Secure Multiparty Computation v1 (opens in new window)

Improved methods for secure multi-party computation.Type: R+OTHER

Data Hub Design and Data Market v1 (opens in new window)

Description of the PHASE IV AI Data Hub and Data Market.Type: R+OTHER

MaaS Toolbox v1 (opens in new window)

ML algorithms for Model as a Service concept.Type: R + OTHER

Integrated Data Services v1 (opens in new window)

Integration of PHASE IV AI Data ServicesType: R+OTHER

Publications

VisTAD: A Vision Transformer Pipeline for the Classification of Alzheimer’s Disease (opens in new window)

Author(s): Noushath Shaffi, Vimbi Viswan, Mufti Mahmud
Published in: 2024 International Joint Conference on Neural Networks (IJCNN), 2024
Publisher: IEEE
DOI: 10.1109/IJCNN60899.2024.10650975

Understanding Feature Importance of Prediction Models Based on Lung Cancer Primary Care Data (opens in new window)

Author(s): Teena Rai, Yuan Shen, Jun He, Mufti Mahmud, David J Brown, Jaspreet Kaur, Emma O’Dowd, David R Baldwin, Richard Hubbard
Published in: 2024 International Joint Conference on Neural Networks (IJCNN), 2024
Publisher: IEEE
DOI: 10.1109/IJCNN60899.2024.10650819

Characterization of Synthetic Lung Nodules in Conditional Latent Diffusion of Chest CT Scans (opens in new window)

Author(s): Roger Marí Molas, Paula Subías-Beltrán, Carla Pitarch Abaigar, Mar Galofré Cardo, Rafael Redondo Tejedor
Published in: Frontiers in Artificial Intelligence and Applications, Artificial Intelligence Research and Development, 2024
Publisher: IOS Press
DOI: 10.3233/FAIA240408

Does Differentially Private Synthetic Data Lead to Synthetic Discoveries? (opens in new window)

Author(s): Ileana Montoya Perez, Parisa Movahedi, Valtteri Nieminen, Antti Airola, Tapio Pahikkala
Published in: Methods of Information in Medicine, Issue 63, 2024, ISSN 0026-1270
Publisher: Georg Thieme Verlag KG
DOI: 10.1055/a-2385-1355

Cognitive Computation (opens in new window)

Author(s): Md. Easin Arafat; Md. Wakil Ahmad; S. M. Shovan; Towhid Ul Haq; Nazrul Islam; Mufti Mahmud; M. Shamim Kaiser
Published in: Accurate Prediction of Lysine Methylation Sites Using Evolutionary and Structural-Based Information, 2024, ISSN 1866-9956
Publisher: Springer Nature
DOI: 10.1007/S12559-024-10268-2

Ensemble of vision transformer architectures for efficient Alzheimer’s Disease classification (opens in new window)

Author(s): Noushath Shaffi; Vimbi Viswan; Mufti Mahmud
Published in: Brain Informatics, 2024, ISSN 2198-4018
Publisher: Springer Open
DOI: 10.1186/S40708-024-00238-7

Towards practical federated learning and evaluation for medical prediction models (opens in new window)

Author(s): Andrei Kazlouski, Ileana Montoya Perez, Faiza Noor, Mikael Högerman, Otto Ettala, Tapio Pahikkala, Antti Airola
Published in: International Journal of Medical Informatics, Issue 204, 2025, ISSN 1386-5056
Publisher: Elsevier BV
DOI: 10.1016/J.IJMEDINF.2025.106046

Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection (opens in new window)

Author(s): Viswan Vimbi; Noushath Shaffi; Mufti Mahmud
Published in: Brain Informatics, 2024, ISSN 2198-4026
Publisher: Springer Nature
DOI: 10.1186/s40708-024-00222-1

Benchmarking Evaluation Protocols for Classifiers Trained on Differentially Private Synthetic Data (opens in new window)

Author(s): Parisa Movahedi, Valtteri Nieminen, Ileana Montoya Perez, Hiba Daafane, Dishant Sukhwal, Tapio Pahikkala, Antti Airola
Published in: IEEE Access, Issue 12, 2024, ISSN 2169-3536
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/ACCESS.2024.3446913

Medical AI in the EU: Regulatory Considerations and Future Outlook (opens in new window)

Author(s): Ranttila, Pertti; Sahebi, Golnaz; Kontio, Elina; Salmi, Jussi
Published in: AI - Ethical and Legal Challenges [Working Title], 2024, ISBN 978-0-85466-497-9
Publisher: IntechOpen
DOI: 10.5772/INTECHOPEN.1007443

Medical AI in the EU: Regulatory Considerations and Future Outlook (opens in new window)

Author(s): Pertti Ranttila, Golnaz Sahebi, Elina Kontio, Jussi Salmi
Published in: AI - Ethical and Legal Challenges [Working Title], 2024
Publisher: IntechOpen
DOI: 10.5772/intechopen.1007443

Response to Letter by Dehaene et al. on Synthetic Discovery is not only a Problem of Differentially Private Synthetic Data (opens in new window)

Author(s): Ileana Montoya Perez, Parisa Movahedi, Valtteri Nieminen, Antti Airola, Tapio Pahikkala
Published in: Methods of Information in Medicine, 2025
Publisher: Georg Thieme Verlag
DOI: 10.1055/A-2540-8346

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