Knowledge graph driven medicine recommendation system using graph neural networks on longitudinal medical records.

ABSTRACT: Medicine recommendation systems are designed to aid healthcare professionals by analysing a patient's admission data to recommend safe and effective medications. These systems are categorised into two types: instance-based and longitudinal-based. Instance-based models only consider the current admission, while longitudinal models consider the patient's medical history. Electronic Health Records are used to incorporate medical history into longitudinal models. This project proposes a novel Knowledge Graph-Driven Medicine Recommendation System using Graph Neural Networks, KGDNet, that utilises longitudinal EHR data along with ontologies and Drug-Drug Interaction knowledge to construct admission-wise clinical and medicine Knowledge Graphs for every patient. Recurrent Neural Networks are emplo

SUBMITTER: Mishra R 

PROVIDER: S-EPMC11511869 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature