Project: Artificial intelligence for personalised medicine in depression - analysis and harmonization of clinical research data for robust multimodal patient profiling for the prediction of therapy outcome
| Acronym | ArtiPro (Reference Number: ERAPERMED2021-357) |
| Duration | 01/05/2022 - 01/05/2025 |
| Project Topic | Personalized medicine aims to predict therapeutic response by biomarker signatures that can be grounded in a biological or genetic profile, personal factors (life events, experience with former drug therapies), comorbidity, metabolic profiles, and molecular signatures. Many studies have been carried out in the field of treatment of depression to stratify patients in order to find a personalized treatment option. However, we are still far away from being able to predict the individual diagnostic subgroup, the course of depressive disease or treatment outcome. This project aims to establish an artificial intelligence platform that brings together data from clinical research on biomarker signatures and therapeutic outcome with the purpose of identifying robust multimodal biomarkers and outcomes for depression. We will combine material from existing clinical studies with two purposes in mind. The first is to extend biomarker dimensions in the pooled study cohorts by combining multiple modalities, the second to fill biomarker gaps across studies and complement biomarkers with the newest molecular technologies. The results will be combined into a single data platform that enables the use of large multimodal datasets to develop predictive models of symptom domains and outcome data, thus enhancing the impact of these data relative to what would be possible in the original individual datasets. Applied to the resulting extended big data repository, artificial intelligence approaches will be investigated to identify novel biomarker index sets predictive of outcomes, creating a base for the development of a decision support system for personalized therapy. Standard outcome measures of relevance to policy makers, regulatory, and patient organizations will be defined by the analyses from the data integration platform. The specific ethical and legal requirements will be identified that need to be fulfilled when developing predictive patient profiles with the help of AI. |
| Network | ERA PerMed |
| Call | 4th Joint Transnational Call for Proposals (2021) |
Project partner
| Number | Name | Role | Country |
|---|---|---|---|
| 1 | University Hospital RWTH Aachen | Coordinator | Germany |
| 2 | School of Medicine, University of Zagreb | Partner | Croatia |
| 3 | Federal Institute for Drugs and Medical Devices, BfArM | Partner | Germany |
| 4 | IRCCS Istituto delle Scienze Neurologiche di Bologna | Partner | Italy |
| 5 | University Innsbruck | Partner | Austria |
| 6 | Tel Aviv University(TAU) | Partner | Israel |
| 7 | Diakonhjemmet Hospital | Partner | Norway |