We propose a novel “Semantic Deep Learning” method to analyze the electronic health records of real patients. Our previous work as successfully used a hypergraph- based approach in the clinical text notes from Stanford Hospital’s Clinical Data Warehouse (STRIDE).
In the second year, we will focus on domain ontology-specific deep learning methods, integrating contemporary deep learning model designs with the existing SMASH healthcare ontology and corresponding data, to structure both model input and potentially output in order to develop meaningful semantic explanations that compare favorably with the state of the art.
In this project, we will design ontology-based interpretable deep models for consumer complaint explanation and analysis. The main idea of our algorithms is to consider domain knowledge in the design of deep learning models and utilize domain ontologies for explaining the deep learning models and results through casual modeling.
In this project, we will develop innovative medical embedding learning algorithms and make knowledge alignment across multiple languages in the medical domain. Our learning algorithms will fully exploit the semantic similarity for knowledge alignment across languages.
The objectives of this project involve developing a customer service ontology represented as knowledge graph and building an automatic customer service system backed by the knowledge graph.