Authors:
Michela Venturini
1
;
2
;
Felipe Nakano
1
;
2
and
Celine Vens
1
;
2
Affiliations:
1
KU Leuven, Campus KULAK, Department of Public Health and Primary Care, Etienne Sabbelaan 53, 8500 Kortrijk, Belgium
;
2
Itec, imec research group at KU Leuven, Etienne Sabbelaan 53, 8500 Kortrijk, Belgium
Keyword(s):
Data Scarcity, Right-censoring, Survival Analysis, Multi-target Regression.
Abstract:
Multi-event survival analysis is an under-explored field in literature, typically addressed by modeling each event independently or implying specific event settings. In this context, problem transformations approaches offer a promising alternative to rephrase the setting into standard multi-target regression. Nevertheless, they also suffer from the intrinsic presence of partial information in time-to-event data, since their application often requires the exclusion of censored observations, thus potentially discarding valuable information. In this work, we propose a novel Problem Transformation Approach for Multi-event Survival analySis (PT-MESS), which is capable of exploiting partial information, by encoding the survival outcome in a risk score based on the time-to-event distribution estimation. This approach allows the use of any multi-target machine learning model to address the original survival task. Using random forest as the underlying model, we conducted experiments using rea
l-data multiple benchmarks from the medical domain and synthetic datasets. Our results revealed that PT-MESS provides superior or competitive results compared to competitors from the literature, especially when the events considered had a similar survival distribution.
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