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SurvTRACE: Transformers for Survival Analysis with Competing Events
Zifeng Wang
,
Jimeng Sun
Siebel School of Computing and Data Science
Coordinated Science Lab
Neuroscience Program
Biomedical and Translational Sciences
Research output
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Dive into the research topics of 'SurvTRACE: Transformers for Survival Analysis with Competing Events'. Together they form a unique fingerprint.
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Keyphrases
Survival Analysis
100%
Transformer
100%
Competing Events
100%
Clinical Trial Design
33%
Disease Diagnosis
33%
Survival Probability
33%
Selection Bias
33%
Time Duration
33%
Events of Interest
33%
Attention Mechanism
33%
Shared Representations
33%
Multi-task Learning
33%
Survival Data
33%
Event Scenario
33%
Survival Distribution
33%
Multiple Diseases
33%
Transformer Model
33%
Auxiliary Task
33%
Interpretable Attention
33%
Treatment Development
33%
Multi-event
33%
SEER Database
33%
METABRIC
33%
Mathematics
Survival Analysis
100%
Covariate
33%
Trial Design
33%
Superiority
33%
Survival Probability
33%
Confounders
33%
Survival Data
33%
Survival Distribution
33%
Learning Task
33%
Survival Rate
33%
Computer Science
Survival Probability
100%
Multitask Learning
100%
Attention (Machine Learning)
100%
Neuroscience
Survival Analysis
100%
Treatment Development
33%
Comorbidity
33%