Identifying and indexing discriminative features for disease progression in observational data

A computer-implemented method, comprising:

• building, by a system operatively coupled to a processor, a preliminary disease progression model based on observational patient data;
• ranking, by the system, discriminative powers of clinical features for respective pairs of disease stages;
• and
• employing, by the system, the ranked discriminative powers of clinical features to generate a final disease progression model, wherein generation of the final disease progression model comprises:
• determination of component features for data-driven disease stage segmentation;
• assignment of respective observations to one of the disease stages to generate disease progression modeling resulting in disease stage assignment;
• and
• for respective ones of the clinical features in the observational patient data, employing assigned disease stages as benchmarks to obtain effective sizes of the clinical features for the respective pairs of disease stages.

A computer program product for monitoring disease progression, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• build a preliminary disease progression model based on observational patient data;
• and
• rank discriminative powers of clinical features for respective pairs of disease stages, wherein the model generation component employs the ranked discriminative powers of clinical features to generate a final disease progression model, wherein generation of the final disease progression model comprises by the model generation component:
• determination of component features for data-driven disease stage segmentation;
• assignment of respective observations to one of the disease stages to generate disease progression modeling resulting in disease stage assignment;
• and
• for respective ones of the clinical features in the observational patient data, employing assigned disease stages as benchmarks to obtain effective sizes of the clinical features for the respective pairs of disease stages.