Siamese Survival Analysis with Competing Risks
This addresses a pervasive problem in industries like healthcare and finance by providing a method to handle nonidentifiability in competing risk scenarios, though it appears incremental as it builds on existing deep learning approaches for survival analysis.
The paper tackles survival analysis with competing risks, where multiple adverse events obscure the event of interest, by introducing the Siamese Survival Prognosis Network (SSPN) to estimate personalized risk scores without needing cause-specific survival curves, achieving direct optimization of an approximation to the C-discrimination index.
Survival analysis in the presence of multiple possible adverse events, i.e., competing risks, is a pervasive problem in many industries (healthcare, finance, etc.). Since only one event is typically observed, the incidence of an event of interest is often obscured by other related competing events. This nonidentifiability, or inability to estimate true cause-specific survival curves from empirical data, further complicates competing risk survival analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep learning architecture for estimating personalized risk scores in the presence of competing risks. SSPN circumvents the nonidentifiability problem by avoiding the estimation of cause-specific survival curves and instead determines pairwise concordant time-dependent risks, where longer event times are assigned lower risks. Furthermore, SSPN is able to directly optimize an approximation to the C-discrimination index, rather than relying on well-known metrics which are unable to capture the unique requirements of survival analysis with competing risks.