DefinePK

Abstract

Biomedical data environments are increasingly dominated by heterogeneous information sources varying from structured electronic health records to unstructured clinical notes and medical imaging. This paper introduces a new neurosymbolic database architecture that integrates symbolic reasoning with neural network learning to efficiently process, integrate, and analyze complex biomedical data. The envisaged framework bridges the logical formalism and explainability of symbolic AI with the pattern recognition properties of neural networks to process structured and unstructured biomedical data in one system. Some of the major contributions include a hybrid query processor that elegantly marries logical inference and deep learning approaches to facilitate advanced biomedical queries and a symbolic-neural translator to provide semantic compatibility between representations. In performance testing on clinical entity recognition and semantic retrieval tasks, our architecture outperformed purely symbolic or neural methods by as much as 33% higher accuracy in oncology entity recognition compared to current state-of-the-art systems. This architecture meets essential healthcare needs such as interpretability, data integrity, and security while facilitating advanced analytical capabilities needed for precision medicine and clinical decision support systems.