INVITED SESSION 5: Machine Learning Algorithms for Survival Analysis
Tracks
PLENARY HALL (Friends of Music)
| Tuesday, July 23, 2024 |
| 11:00 AM - 12:40 PM |
| PLENARY HALL (M1 building upper floor) |
Details
This session will focus on the methodological challenges and recent achievements at Survival Analysis and Machine Learning (ML) intersection. Survival analysis is a crucial ‘traditional’ topic in clinical biostatistics, featured in almost all International Society for Clinical Biostatistics (ISCB) conferences through an Invited Session. Traditionally, survival analysis relies on statistical modeling. However, the recent advent of ML offers new ways to analyze censored survival data and new analytical challenges. The main aim of the session is to present recent ML methods developed for the analysis of survival data. For instance, while most ML research primarily focuses on metrics such as ROC-AUC and specific loss functions, calibration is often overlooked. Therefore, comparing existing ML methods for survival data can be highly practical. Another topic of interest is constructing confidence bands for survival curves using deep learning methods for survival data.
Presentations
Prof. Malka Gorfine
Tel Aviv University, Israel
Prof. Dr. Harald Binder
Institute of Medical Biometry and Statistics (IMBI)
Dr. Qixian Zhong
Xiamen University, School of Economics, The Wang Yanan Institute for Studies in Economics, Dept of Finance
Chair
Michal Abrahamowicz
McGill University Medical Centre, Division of Clinical Epidemiology
Dimitris Rizopoulos
Erasmus University Medical Center, Dept of Biostatistics
