Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification
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The medical automatic diagnosis system aims to imitate human doctors in the real diagnostic process. This task is formulated as a sequential decision-making problem with symptom inquiring and disease diagnosis. In recent years, many researchers have used reinforcement learning methods to handle this task. However, most recent works neglected to distinguish the symptom inquiring and disease diagnosing actions and mixed them into one action space. This results in the unsatisfactory performance of reinforcement learning methods on this task. Moreover, there is a lack of a public evaluation dataset that contains various diseases and corresponding information. To address these issues, we first propose a novel method for medical automatic diagnosis with symptom inquiring and disease diagnosing formulated as a reinforcement learning task and a classification task, respectively. We also propose a robust and adaptive method to align the two tasks using distribution entropies as media. Then, we create a new dataset extracted from the MedlinePlus knowledge base. The dataset contains more diseases and more complete symptom information. The simulated patients for experiments are more realistic. Experimental evaluation results show that our method outperforms three recent state-of-the-art methods on different datasets by achieving higher medical diagnosis accuracies with few inquiring turns.
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