OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation
This work addresses medication recommendation for healthcare by enhancing representation learning with medical ontologies, though it is incremental as it builds on existing EHR-based models.
The paper tackles the data sparsity problem in medication recommendation by using logically-pretrained ontology encoders, resulting in improved performance across various models and datasets, including few-shot scenarios.
Most existing medication recommendation models learn representations for medical concepts based on electronic health records (EHRs) and make recommendations with learnt representations. However, most medications appear in the dataset for limited times, resulting in insufficient learning of their representations. Medical ontologies are the hierarchical classification systems for medical terms where similar terms are in the same class on a certain level. In this paper, we propose OntoMedRec, the logically-pretrained and model-agnostic medical Ontology Encoders for Medication Recommendation that addresses data sparsity problem with medical ontologies. We conduct comprehensive experiments on benchmark datasets to evaluate the effectiveness of OntoMedRec, and the result shows the integration of OntoMedRec improves the performance of various models in both the entire EHR datasets and the admissions with few-shot medications. We provide the GitHub repository for the source code on https://anonymous.4open.science/r/OntoMedRec-D123