Prediction of Public Opinion Early Warning Level of Medical Dispute Cases Based on Ontology

Abstract

The court’s trial results of medical disputes are likely to attract social attention. How to effectively evaluate and predict public opinion is the focus of this paper. Combining the case description of medical dispute cases and the judgment results of the court, using natural language processing (NLP) technology, applying ontology knowledge to construct the medical dispute case ontology, based on ontology reasoning method to complete the case elements; then machine learning algorithms and ontology structure are combined to build predictive model of the public opinion warning level of dispute cases. Among them, the domain ontology structure is established for medical dispute cases, which enriches the semantic relationship between case elements, infers incomplete case elements through the definition of reasoning rules, and completes the case structure. Therefore, it has the ability to predict the public opinion warning level of medical dispute cases. It could help government agencies understand the views of the public, the positive and negative effects of the case, and enhance judicial credibility.