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

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.
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Startår

2024

Beviljade finansiering

Markus Perola Orcid -palvelun logo
63 964 €



Rollen i Finlands Akademis konsortium

Övriga parter i konsortiet

Leader
Aalto-universitetet (358958)
247 428 €
Partner
Helsingfors universitet (358999)
183 832 €

Finansiär

Finlands Akademi

Typ av finansiering

Akademiprojekt med särskild inriktning

Övriga uppgifter

Finansieringsbeslutets nummer

359072

Vetenskapsområden

Data- och informationsvetenskap

Forskningsområden

Tietojenkäsittelytieteet