D2.6 - Third report of the Ethics Advisory Board (EAB)
After the last two previous meetings of our Ethics Advisory Board (EAB) -which resulted in reports D2.2 and D2.4-, the EAB members were presented with the updated position with regards to the project objectives and progress, the data protection and ethics oversight and assessments for the project. In this period, GenoMed4All focused its efforts on the finalisation of the Joint Controller Agreements (JCAs), enhancement of the Federated Learning architecture and development of the recommendations on the ethical principles, quality processes and stakeholder engagements for AI development as can be found in deliverable D2.5.
Abstract
Deliverable 2.6 provides the third report of the Ethical Advisory Board for GenoMed4All. The 3rd Meeting took place on Monday 20th November 2023 and provided an opportunity for the project to update the EAB external members on the latest developments over 2023. The key points of development included the completion and signing and execution of three Joint Controller Agreements for each of the Use Cases, further technical developments that place the majority of personal data processing on a Federated Learning infrastructure and finalisation of the approach to develop the Ethical Principles and Quality Assurances for Deliverable 2.5. There was also reflection on the recently enacted Data Governance Act and the forthcoming AI Act and regulations around the European Health Data Space.
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D2.5 - Recommendations on the ethical principles, quality processes and stakeholder engagements for AI development
Deliverable 2.5 describes the ethical principles for GenoMed4All and similar AI driven projects and interventions for managing and risk assessing rare Haematological diseases. This description of principles draws from the European Commission Ethics Guidelines for Trustworthy Artificial Intelligence.
Abstract
Deliverable 2.5 summaries the proposed ethical principles for GenoMed4All and wider multi-omics and clinical data driven projects for rare diseases that involve the training and development of risk prediction and outcome measure algorithms that leverage artificial intelligence. D2.5 goes on to describe the approaches for stakeholder engagement (including clinical practitioners and sites, development teams, AI researchers and patient associations and the wider citizenry). This includes quality processes derived from the ethical principles where consideration of the ethical challenges around autonomy, bias and transparency in data use amongst others are pertinent.
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D2.4 - Second report of the Ethics Advisory Board (EAB)
Deliverable 2.4 provides the second report of the GenoMed4All Ethical Advisory Board (EAB). This report has been compiled after the first meeting of our Ethics Advisory Board (EAB) held on 19th January 2022 -a recap can be found here-, and a subsequent set of meetings in November 2022.
Abstract
Following the completion of the previous meeting and submission of D2.2, GenoMed4All focused its efforts on developing the mapping and ethical principles identified in the previous meeting, but had to address as a priority the changing expectations around GDPR compliance and development of the data sharing agreements. Identification of the roles of GenoMed4All partners had to be prioritised, and a new Joint Controller Agreement Template had to be agreed by the partners. This underpinned the compliance requirements that were being overseen by the EAB. Due to regulatory changes and evolved interpretations in several jurisdictions, including France and Italy respectively, the agreements and approaches had to be updated.
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D9.2 - Interim Communication & Dissemination report
This deliverable intends to act as an update and in-depth review of GenoMed4All’s strategy and framework conceived for Communication, Dissemination and Engagement purposes. The initial design of this framework was previously outlined in GenoMed4All’s Impact Master Plan (D9.1), during the early stages of the project. Now comes the time to reflect on the outreach efforts carried out through this first review period and study its overall performance.
Abstract
This deliverable provides a comprehensive overview of all dissemination and communication actions undertaken to reach a critical mass throughout GenoMed4All’s first review period (M1-M18) and has been updated to include all new developments up until M36. It contains a detailed description of the project’s visual identity, online channels, promotional materials, events and publications, together with an introduction to its onboarding and networking strategies.
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D8.2 - Training programme definition
This deliverable sets the stage for what will become GenoMed4All’s training programme for Artificial Intelligence and precision medicine in Hematology: from the nomination of our Educational Committee to the design and careful planning of the two waves of training webinars (Wave #1 for the public at large and Wave #2 for an expert audience).
Abstract
This deliverable comprises the first phase of the design of the GenoMed4All training programme, which is focused on the definition of the topics, methodologies and training materials. D8.2 is the result of the collaborative work performed by GenoMed4All Educational Scientific/Technical Committee towards the definition of the GenoMed4All training programme.
D2.2 - First Report of the Ethics Advisory Board
Following the introductory meeting of our Ethics Advisory Board (EAB) -you can read all about it here-, their first report (D2.2) has been publicly released.
Abstract
This deliverable provides the first report of the GenoMed4All Ethical Advisory Board (EAB). The report has been compiled after the first meeting of the Ethical Advisory Board held on 19th January 2022. The meeting was presented with the current position with regards the project objectives and progress, data protection and ethics oversight and assessments for the project.
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D4.1 - Hybrid Federated Learning Model
D4.1 is finally out!
This deliverable is the result of months of intense discussions on GenoMed4All’s hybrid Federated Learning framework.
Abstract
The main aim of this deliverable is to define GenoMed4All’s hybrid federated learning model specification expressed in terms of platform requirements, which have been acquired by analyzing in details the project’s objectives as well as by decomposing the stakeholders’ needs through the different use cases’ definition. It includes a description of the requirements elicitation methodology together with the set of technical requirements regarding the federated learning approach of the project’s platform. Moreover, it includes an extensive analysis of the most important federated learning framework solutions already available, and it finally defines the evaluation criteria that have helped in the selection of the most appropriate one for the project’s purposes and needs.
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D6.2 - Preliminary conclusions about Federated Learning applied to clinical data
After an initial phase focused on literature mining and pre-processing, we are happy to share our preliminary conclusions about Federated Learning applied to clinical data as a part of deliverable D6.2.
Abstract
This report comprises the first contributions from different partners on Federated Learning (FL). After a preliminary introductory section where the fundamental procedures and limitations are described, we detail the well-known mathematical foundation of Federated Learning for convex problems. In this case, we present a key algorithm, Alternating Direction Multipliers Method (ADMM), which is able to implement in a distributed way some fundamental problems such as regression (Ridge and LASSO) and classification (Logistic Regression and Support Vector Machines (SVM)). This procedure shares the fundamental approach of FL, which consists of performing some local processing, sharing some intermediate information and updating the local information with some global innovation. In a second step we introduce the extension of this approach to non-convex problems using Bayesian Neural Networks (BNN) where the update is based on the cooperative construction of the posterior of weights from different architectures. Several sections follow where different partners provide different contributions describing our first initiatives on the topic. Some preliminary code from all partners has been uploaded to a common repository to start creating a pool of methods and tools to foster incoming synergies.
D5.1 - Data homogenization requirements and specifications
D5.1 is now out!
Abstract
Considering the wide range of repositories that GenoMed4All will need, homogenizing data formats from different sources and systems (e.g. EHRs, images, genomic data, etc.) is essential. This deliverable sets the requirements for the data homogenization processes paving the way to a common approach for a data lake, based on the FHIR standard. FHIR enables almost direct integration with most of the current information systems and automatic data homogenization and enrichment to facilitate data processing and analytics.
D2.1 – Data processing, Data Management Plan and GDPR compliance report
Deliverable D2.1 is officially out!
Abstract
Monitoring data protection compliance for an initiative like GenoMed4All can only be successful if there is a swift understanding of compliance across all partners and a clear concordance. To that end, the need for a reliable and consistent approach to understand data flows, sources, recipients and roles with regards overall responsibility (including Data Controllers, Processors and Custodians) is clear. The consortium has therefore agreed that using a rigorous Data Protection Impact Assessment (DPIA) Template is key to help map out not only data flows, but also responsibilities and the prerequisites for ensuring data processing is lawful.
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