Description
MODMAP (Multi-Omics Data MAnagement Platform) is a secure, end-to-end digital infrastructure developed within the HT-Advance project (https://ht-advance.eu/) to enable machine learning (ML)-driven personalised decision support in hypertension care. Building on the clinical and scientific foundation established by the Horizon 2020 ENS@T-HT program, which identified key biomarkers for endocrine hypertension (EHT) subtypes—such as primary aldosteronism, pheochromocytoma, Cushing’s syndrome, and primary hypertension (PHT) —MODMAP has enabled the streamlined capture, integration, and interpretation of large-scale multi-omics data. It supports clinical trials that explore effectiveness of the pre-trained ENS@T ML model vs usual clinical care and the treatment response of PHT medication. MODMAP connects clinicians and omics laboratories through a unified platform that facilitates sample tracking, omics data collection (plasma/urinary steroids, catecholamines, microRNAs, genotypes) and disease prediction delivery. Data are securely stored and managed in an ISO27001-accredited environment in Health Informatics Centre (HIC) at University of Dundee.MODMAP ensures a seamless data flow from patient blood and urine collection (via Castor integration) to ML-based EHT predictions, which are returned to clinicians via a user-friendly interface. The platform incorporates a validated trained model from ENS@T-HT, optimized for accuracy, specificity, and complexity, and deployed using R-based wrappers. Built using agile software development practices—including version control, continuous integration, and testing frameworks—MODMAP maintains high standards for reliability, reproducibility, and model performance. It also ensures that all software and ML models are properly versioned and validated.
As an early adopter of ML methodologies in clinical practice with compliance to the new EU AI Act, MODMAP exemplifies how robust digital tools can translate biomarker research into actionable healthcare insights. It serves not only as a technical solution for personalised hypertension management but also as a model for securely and effectively integrating machine learning into clinical workflows.
| Period | 4 Sept 2025 |
|---|---|
| Event title | Final Masterclass and Final Conference of the CA20122: Joint 24th ENS@T Scientific Meeting and 4th Harmonisation Meeting |
| Event type | Conference |
| Location | Belgrade, SerbiaShow on map |
| Degree of Recognition | International |