This paper proposes REFUEL, a reinforcement learning method with two techniques: {\em reward shaping} and {\em feature rebuilding}, to improve the performance of online symptom checking for disease diagnosis. Reward shaping can guide the search of policy towards better directions. Feature rebuilding can guide the agent to learn correlations between features. Together, they can find symptom queries that can yield positive responses from a patient with high probability. Experimental results justify that the two techniques in REFUEL allows the symptom checker to identify the disease more rapidly and accurately.

The authors describe an RL architecture comprised of reward shaping plus representation learning that is used to solve an active classification problem, framed as "diagnosis." In this setting, an agent can measure the value of "symptoms" at some cost, and eventually makes a prediction of what disease is present. The architecture is intended to take advantage of the property that symptoms are sparse but correlated. Reward shaping is used to help the agent learn to quickly find symptoms that are present, while the correlations are used to avoid having the agent measure symptoms that are already "known" based on already-measured ones with high certainty. Experimental results demonstrate a substantial improvement over prior work.

This paper proposes REFUEL, a reinforcement learning method with two techniques: {\em reward shaping} and {\em feature rebuilding}, to improve the performance of online symptom checking for disease diagnosis. Reward shaping can guide the search of policy towards better directions. Feature rebuilding can guide the agent to learn correlations between features. Together, they can find symptom queries that can yield positive responses from a patient with high probability. Experimental results justify that the two techniques in REFUEL allows the symptom checker to identify the disease more rapidly and accurately.