Deep Reinforcement Learning for Cost-Effective Medical Diagnosis

Understand

Dynamic diagnosis is desirable when medical tests are costly or time-consuming.

• In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost.
• Clinical diagnostic data are often highly imbalanced; therefore, we aim to maximize the $F_1$ score instead of the error rate.
• However, optimizing the non-concave $F_1$ score is not a classic RL problem, thus invalidates standard RL methods.