From: Predicting lung cancer survival prognosis based on the conditional survival bayesian network
Notation | Description |
---|---|
\(n\) | number of features |
\(N\) | number of samples |
\(p\) | number of predictors in CPH model |
\({{\varvec{X}}}_{{\varvec{i}}}\) | \(1\times p\) vector of features for patient \(i\) in CPH model |
\({{\varvec{X}}}_{{\varvec{B}}{\varvec{N}}}\) | \(1\times n\) vector of variables in Bayesian network model |
\(T\) | observed time |
\(E\) | indicator of event status |
\({x}_{i}\) | the \(i\)-th variable for each patient |
\(h(t)\) | the hazard function |
\({h}_{0}(t)\) | the baseline hazard function |
\({H}_{0}(t)\) | baseline cumulative hazard function |
\(S(t)\) | survival probability function |
\({S}_{0}(t)\) | baseline survival function |