A DI recovery management system generates a plurality of ranked claimant records and recovery scores. A predictive machine learning model inputs disability income claim data and disability income claimant data into an event history model utilizing discrete-time survival analysis in conjunction with a gradient boosting machine learning model. The claim termination event is one of a plurality of preselected recovery events that indicate that a claimant has achieved return to work capacity. Claimant data used in modeling includes diagnosis data representative of workplace disability duration guidelines. The predictive machine learning model is continually trained using updated disability income claims data. The training procedure transforms claimant records extracted from a DI claims database into a longitudinal format that includes multiple person-year records corresponding to each claimant record. A DI recovery dashboard displays a hazard plot representing a conditional probability over time that a claimant will realize a claim termination event.