Towards a Robust Medical Record System: Integrating Logical Reasoning for Trustworthy Data Management

Aiming at the problem of low-quality electronic medical record(EMRs), the medical knowledge graph is used to enhance the quality and trustworthiness of EMRs. We propose a novel EMR quality control method that integrates a knowledge graph with deep learning techniques and complement it with a system implementation. The entities and relationships are extracted by the End to End Medical Information Extraction based on Knowledge Base (MIE-KB), which leverages an encoder-decoder framework and self-supervised pre-training model based on BERT. We introduce a bidirectional QC system for assessing the content normality and diagnostic rationality of medical records to classify and detect contradictions. By using more than 500,000 desensitized medical records covering 129 diseases in 7 categories, deep learning methods are used to learn the relationship between the desensitized medical records and the diagnosis features, and to determine whether the diagnosis is reasonable through the inference of medical knowledge mapping. We use the UMLS and other standards to evaluate the extraction accuracy and the diagnostic accuracy of the QC system. The system evaluation results show that the system can effectively boost the quality control effect of EMRs. The end-to-end pre-training model and bidirectional QC system improve the quality of EMRs in primary hospitals.