Multi-Source Survival Domain Adaptation
- URL: http://arxiv.org/abs/2212.00424v1
- Date: Thu, 1 Dec 2022 10:55:22 GMT
- Title: Multi-Source Survival Domain Adaptation
- Authors: Ammar Shaker, Carolin Lawrence
- Abstract summary: We introduce a new survival metric and the corresponding discrepancy measure between survival distributions.
Our experiments on two cancer data sets reveal a superb performance on target domains, a better treatment recommendation, and a weight matrix with a plausible explanation.
- Score: 11.57423546614283
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Survival analysis is the branch of statistics that studies the relation
between the characteristics of living entities and their respective survival
times, taking into account the partial information held by censored cases. A
good analysis can, for example, determine whether one medical treatment for a
group of patients is better than another. With the rise of machine learning,
survival analysis can be modeled as learning a function that maps studied
patients to their survival times. To succeed with that, there are three crucial
issues to be tackled. First, some patient data is censored: we do not know the
true survival times for all patients. Second, data is scarce, which led past
research to treat different illness types as domains in a multi-task setup.
Third, there is the need for adaptation to new or extremely rare illness types,
where little or no labels are available. In contrast to previous multi-task
setups, we want to investigate how to efficiently adapt to a new survival
target domain from multiple survival source domains. For this, we introduce a
new survival metric and the corresponding discrepancy measure between survival
distributions. These allow us to define domain adaptation for survival analysis
while incorporating censored data, which would otherwise have to be dropped.
Our experiments on two cancer data sets reveal a superb performance on target
domains, a better treatment recommendation, and a weight matrix with a
plausible explanation.
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