<p>A systematic survival analysis approach is developed to integrate the fundamental concepts from conventional statistics and the more explanatory, algorithmic, and computational techniques offered by optimization and machine learning. The proposed methodology enables the discovery of the combinations of significant features associated with time to an event of interest and produces combinatorial patterns in large-scale datasets. The combinatorial survival patterns are obtained as algorithmic solutions of a mixed integer linear programming problem. Application of the developed pattern-based survival analysis approach is demonstrated in benchmark medical datasets.</p>