Training like Playing: A Reinforcement Learning And Knowledge Graph-based framework for building Automatic Consultation System in Medical Field
This addresses medical consultation automation, but appears incremental as it combines existing techniques without specifying concrete improvements.
The authors developed a medical consultation system framework combining knowledge graphs and reinforcement learning to dynamically diagnose patients based on collected evidence, reporting that it achieved good experimental results.
We introduce a framework for AI-based medical consultation system with knowledge graph embedding and reinforcement learning components and its implement. Our implement of this framework leverages knowledge organized as a graph to have diagnosis according to evidence collected from patients recurrently and dynamically. According to experiment we designed for evaluating its performance, it archives a good result. More importantly, for getting better performance, researchers can implement it on this framework based on their innovative ideas, well designed experiments and even clinical trials.