Medical pathways models mined by complex healthcare logs

Obtaining medical pathways from a large number of medical logs has become a current research hotspot. In this article, we proposed a method that combines trace clustering, process discovery and neural network to discover medical pathway models from complex medical logs. The source medical logs were structured as XES event logs first. Cases with similar medical behavior were aggregated by trace clustering. Use process mining to generate process models. Extract reasonable medical pathways from the process models. Neural network was used to determine the proportional characteristics of medical pathways. Combine the above to form a usable medical pathway model. The results of the experiments show that the average simplicity of the generated process model is 0.695, the average accuracy of the neural network models is 93.44%, and the medical pathway model score is about 0.879.