An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

Information Science, Computer Science, Artificial Intelligence, Medicine, Infectious Diseases,
knowledge graph   cognitive reasoning   explainability   general practice   electronic health records  

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice Background General medicine, as an important part of community and family healthcare, covers different ages, genders, organ systems, and various diseases. Its core concept is human-centered, family-based, emphasizing long-...

Knowledge-Enhanced Graph Topic Transformer for Explainable Biomedical Text Summarization

Information Science, Computer Science, Artificial Intelligence, Medicine, Environmental and Public Health, Medical Laboratory Science, Life Sciences, Biochemistry,
biomedical text summarization   domain knowledge   graph neural network   explainability   pre-trained language models  

Application of Knowledge-Enhanced Graph Topic Transformer in Interpretable Biomedical Text Summarization Research Background Due to the continuous increase in the volume of biomedical literature, the task of automatic biomedical text summarization has become increasingly important. In 2021 alone, 1,767,637 articles were published in the PubMed data...

Critical Observations in Model-Based Diagnosis

Information Science, Computer Science, Artificial Intelligence,
explanation   explainability   ai   diagnosis   model-based   critical observation  

In model-based fault diagnosis, the ability to identify the key observational data that leads to system abnormalities is highly valuable. This paper introduces a framework and algorithm for identifying key observational data. The framework determines which observations are crucial for diagnosis by abstracting the raw observational data into “sub-ob...