Population-scale networks to improve disease diagnosis and treatment - Privacy-preserving meta-learning of user models on graphs

Akronym

PRIMUS

Bidragets beskrivning

We aim at enabling better treatment of diseases by taking into account the information in the family and other networks of patients, and more generally ask how can risk assessments and treatments be best improved by taking into account the unique population-wide data of Finland. This requires developing new machine learning methods, and developing them to be privacy-preserving. We start with the leading cause of death in Finland, cardiovascular diseases, and develop the methods to be applicable not only to other diseases but also more widely in personalized decision making problems across fields.
Visa mer

Startår

2024

Slutår

2026

Beviljade finansiering

Samuel Kaski
247 428 €



Rollen i Finlands Akademis konsortium

Övriga parter i konsortiet

Partner
Institutet för hälsa och välfärd (359072)
63 964 €
Partner
Helsingfors universitet (358999)
183 832 €

Finansiär

Finlands Akademi

Typ av finansiering

Akademiprojekt med särskild inriktning

Övriga uppgifter

Finansieringsbeslutets nummer

358958

Vetenskapsområden

Data- och informationsvetenskap

Forskningsområden

Tietojenkäsittelytieteet