Effective Medical Test Suggestions Using Deep Reinforcement Learning

05/30/2019 ∙ by Yang-En Chen, et al. ∙ 0 ∙ share

Effective medical test suggestions benefit both patients and physicians to conserve time and improve diagnosis accuracy. In this work, we show that an agent can learn to suggest effective medical tests. We formulate the problem as a stage-wise Markov decision process and propose a reinforcement learning method to train the agent. We introduce a new representation of multiple action policy along with the training method of the proposed representation. Furthermore, a new exploration scheme is proposed to accelerate the learning of disease distributions. Our experimental results demonstrate that the accuracy of disease diagnosis can be significantly improved with good medical test suggestions.

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Effective Medical Test Suggestions Using Deep Reinforcement Learning

Authors

Control with Distributed Deep Reinforcement Learning: Learn a Better Policy

EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection

TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning

Learning medical triage from clinicians using Deep Q-Learning

Learning Reinforced Agents with Counterfactual Simulation for Medical Automatic Diagnosis

Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning

Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation

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