DxFormer: a decoupled automatic diagnostic system based on decoder-encoder transformer with dense symptom representations.

ABSTRACT:

Motivation

Symptom-based automatic diagnostic system queries the patient's potential symptoms through continuous interaction with the patient and makes predictions about possible diseases. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods focus on disease diagnosis while ignoring the importance of symptom inquiry. Although these systems have achieved considerable diagnostic accuracy, they are still far below its performance upper bound due to few turns of interaction with patients and insufficient performance of symptom inquiry. To address this problem, we propose a new automatic diagnostic framework called DxFormer, which decouples symptom inquiry and disease diag

SUBMITTER: Chen W 

PROVIDER: S-EPMC9825744 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature