Learning Interpretable Strategies in the Presence of Existing Domain Knowledge

A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to implement a dynamic treatment regime generation engine for learning interpretable strategies in the presence of existing domain knowledge, wherein the computer readable program causes the data processing system to:

• compute a discounted health variable with a penalty for deviating from clinical guidelines based on a distance function representing an allowed deviation from the clinical guidelines;
• apply, by a model builder executing within the data processing system, reinforcement learning techniques on the discounted health variable to generate a reinforcement learning (RL) model for generating dynamic treatment regimes;
• determine, by the dynamic treatment regime generation engine, for a patient for a plurality of times, an unconstrained next action in a treatment regime using the RL model with no distance function, a partially guideline compliant next action in the treatment regime with allowed deviation from the guidelines, and a guideline compliant next action in the treatment regime that adheres to the guidelines;
• generate, by a presentation layer within the dynamic treatment regime generation engine, an outcome output display based on the determined next action in a treatment regime using the RL model with no distance function, optimal next action in the treatment regime with allowed deviation from the guidelines, and next action in the treatment regime that adheres to the guidelines;
• and
• present, by the presentation layer, the outcome output display to a user.

A data processing system comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to implement a dynamic treatment regime generation engine for learning interpretable strategies in the presence of existing domain knowledge, wherein the instructions cause the processor to:
• compute a discounted health variable with a penalty for deviating from clinical guidelines based on a distance function representing an allowed deviation from the clinical guidelines;
• apply, by a model builder executing within the data processing system, reinforcement learning techniques on the discounted health variable to generate a reinforcement learning (RL) model for generating dynamic treatment regimes;
• determine, by the dynamic treatment regime generation engine, for a patient for a plurality of times, an unconstrained next action in a treatment regime using the RL model with no distance function, a partially guideline compliant next action in the treatment regime with allowed deviation from the guidelines, and a guideline compliant next action in the treatment regime that adheres to the guidelines;
• generate, by a presentation layer within the dynamic treatment regime generation engine, an outcome output display based on the determined next action in a treatment regime using the RL model with no distance function, optimal next action in the treatment regime with allowed deviation from the guidelines, and next action in the treatment regime that adheres to the guidelines;
• and
• present, by the presentation layer, the outcome output display to a user.