Interactive machine learning from multiple biodata sources

Project Details

Description

Precision medicine, which is tailored to individual patients, brings a difficult challenge to data analysis methods: The goal is to learn to predict effectiveness of treatments using data with extremely small sample size, even equal to one patient, but an extremely large number of variables. Additional data exist but their relevance is unknown, access may be restricted due to privacy, and the complex models required for good predictivity are computationally heavy. Prof. Kaski develops computational, so-called machine learning methods for analysing multiple data sources, taking into account tacit knowledge of domain experts with interactive modelling. Along with the increasing data-intensivity, similar problems have become very general in other fields of science and services as well, starting from information retrieval.
Short titleKaski Samuel AP
StatusFinished
Effective start/end date01/01/201631/08/2021

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

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