  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.
