OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation
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
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Hybrid ANN-SNN Pipeline with Local Plasticity
This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs) to enable high-performance spiking neural networks (SNNs). The architecture coup…
Learning biologically relevant features in a pathology foundation model using sparse autoencoders
Pathology plays an important role in disease diagnosis, treatment decision-making and drug development. Previous works on interpretability for machine learning models on pathology images have revolved around methods such…
Asking without Telling: Exploring Latent Ontologies in Contextual Representations
The success of pretrained contextual encoders, such as ELMo and BERT, has brought a great deal of interest in what these models learn: do they, without explicit supervision, learn to encode meaningful notions of linguist…
Language Models as Ontology Encoders
OWL (Web Ontology Language) ontologies which are able to formally represent complex knowledge and support semantic reasoning have been widely adopted across various domains such as healthcare and bioinformatics. Recently…
Transfer LearningGeRA: Label-Efficient Geometrically Regularized Alignment
Pretrained unimodal encoders incorporate rich semantic information into embedding space structures. To be similarly informative, multi-modal encoders typically require massive amounts of paired data for alignment and tra…