A novel DL-based algorithm integrating medical knowledge graph and doctor modeling for Q&A pair matching in OHP

Using AI technology to automatically match Q&A pairs on online health platforms (OHP) can improve the efficiency of doctor-patient interaction. However, previous methods often neglected to fully exploit rich information contained in OHP, especially the medical expertise that could be leveraged through medical text modeling. Therefore, this paper proposes a model named MKGA-DM-NN, which first uses the named entities of the medical knowledge graph (KG) to identify the intention of the problem, and then uses graph embedding technology to learn the representation of entities and entity relationships in the KG. The proposed model also employs the relationship between entities in KG to optimize the hybrid attention mechanism. In addition, doctors' his-torical Q&A records on OHP are used to learn modeling doctors' expertise to improve the ac-curacy of Q&A matching. This method is helpful to bridge the semantic gap of text and improve the accuracy and interpretability of medical Q&A matching. Through experiments on a real dataset from a Chinese well-known OHP, our model has been verified to be superior to the baseline models. The accuracy of our model is 4.4% higher than the best baseline model. The cost -sensitive error of our model is 13.53% lower than that of the best baseline model. The ablation experiment shows that the accuracy rate can be significantly improved by 8.72% by adding the doctor modeling module, and the cost-sensitive error can be significantly reduced by 17.27% by adding the medical KG module.

Automated summary

Using AI technology to automatically match Q&A pairs on online health platforms can improve doctor-patient interaction efficiency. This paper proposes a model named MKGA-DM-NN, which leverages medical knowledge graph (KG) entities, graph embedding technology, and doctors' historical Q&A records to improve the accuracy of Q&A matching. Experimental results show that our model outperforms baseline models, achieving 4.4% higher accuracy and 13.53% lower cost-sensitive error. Adding doctor modeling module improves accuracy by 8.72%, while adding medical KG module reduces cost-sensitive error by 17.27%.