Hyvä Suomen Biostatistiikan seura ry:n jäsen,

Syystapaaminen ja lyhytkurssi

Aiemmin ilmoitetusta poiketen seuran syystapaaminen ja lyhytkurssi aiheesta ”Time to Event Outcome with Statistical Learning” on siirretty toteutettavaksi tiistaina 11.1.2022. Kurssin pitää professori Malka Gorfine (Tel Aviv University, Israel). Kurssi on enlanninkielinen.

Kurssi järjestetään Tieteiden talon väistötiloissa (Aalto-yliopisto Töölö, Runeberginkatu 14-16) klo 12:15 – 17:00. Kurssin jälkeen siirrymme Ravintola Töölöön yhteiselle illalliselle ja vapaamuotoisen illanvieton pariin.

Tarkemmat tiedot osallistumisesta sekä ilmoittautumisesta lähetetään myöhemmin. Alta löydätte tiedot kurssin alustavasta sisällöstä.

Time to Event Outcome with Statistical Learning

Machine learning of time-to-event outcome methods take advantage of recent development of machine learning and optimization to learn the association structure between survival times and covariates in a flexible manner. This course will cover contemporary methodologies for machine learning with time-to-event outcomes that either compete or complement with traditional survival methods. This includes techniques for high dimensional data, as well as methods appropriate for huge number of observations. Participants will learn about the most effective machine learning of time-to-event outcome techniques. Additionally, they will learn about not only the theoretical underpinnings of learning in survival analysis, but also gain the practical know-how needed to quickly and powerfully apply these techniques to modern datasets such as electronic health records (EHR). Topics include: a brief review of existing conventional time-to-event methods (non-parametric, semi-parametric and parametric models); performance evaluation metrics; regularized time-to-event regression methods; survival trees ensemble (bagging, random forest, boosting) and neural networks.

COURSE OUTLINE
Session I: A brief review of existing conventional time-to-event methods, including non-parametric, semi-parametric, and parametric models. Performance evaluation metrics.
Session II: Regularized time-to-event regression methods such as Lasso, ridge, elastic net and OSCAR.
Session III: Survival trees ensemble. This includes bagging survival trees, random survival forests and boosting.
Session IV: Deep learning and (if time permits) support vector machine.

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