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Found 5 papers, 2 papers with code
A optimization framework for herbal prescription planning based on deep reinforcement learning
We constructed a high-quality benchmark dataset for sequential diagnosis and treatment of diabetes and evaluated PrescDRL against this benchmark.
Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction
In this paper, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end Knowledge graph completion model for Disease Gene Prediction using interactional tensor decomposition (called KDGene).
A Pre-training Framework for Knowledge Graph Completion
Knowledge graph completion (KGC) is one of the effective methods to identify new facts in knowledge graph.
Network medicine framework reveals generic herb-symptom effectiveness of Traditional Chinese Medicine
We validate our framework with real-world hospital patient data by showing that (1) shorter network distance between symptoms of inpatients correlates with higher relative risk (co-occurrence), and (2) herb-symptom network proximity is indicative of patients' symptom recovery rate after herbal treatment.
TCMPR: TCM Prescription recommendation based on subnetwork term mapping and deep learning
In addition, the comprehensive experiments of TCMPR with different hyper parameters (i. e., feature embedding, feature dimension and feature fusion) that demonstrates that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.
