Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification. (arXiv:2112.00733v1 [cs.LG])

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.

Source: https://arxiv.org/abs/2112.00733

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