Training like Playing: A Reinforcement Learning And Knowledge Graph-based framework for building Automatic Consultation System in Medical Field

Abstract

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.

Knowledge Graph-Based Framework for Decision Making Process with Limited Interaction

“…Abu-Salih continues to argue that the underlying structure of the KG enables better comprehension, reasoning, and interpretation of knowledge for both humans and machines. These features attract more and more researchers in recent years to use KG as the main means to deal with real-life problems in various fields, such as threat detections [34], interactive recommendations [35], healthcare and medical consultations [36][37][38], service system developments [39], designing decision support systems [32], and more. We elaborate on KG usages, which are similar to our framework, in the following paragraphs.…”

“…Huang et al [37] introduced a framework for an AI-based medical consultation system with knowledge graph embedding. Their framework implementation leverages knowledge organized as a graph to have diagnosis according to evidence collected from patients recurrently and dynamically.…”