Summary of Ontomedrec: Logically-pretrained Model-agnostic Ontology Encoders For Medication Recommendation, by Weicong Tan et al.
OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation
by Weicong Tan, Weiqing Wang, Xin Zhou, Wray Buntine, Gordon Bingham, Hongzhi Yin
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes OntoMedRec, a model that addresses the data sparsity problem in medication recommendation models. The approach uses medical ontologies to learn representations for medications and make recommendations. Unlike existing models that learn from electronic health records (EHRs), OntoMedRec integrates logical pre-training with model-agnostic encoders to improve performance. The authors conduct experiments on benchmark datasets, showing that OntoMedRec outperforms other models in both entire EHR datasets and admissions with few-shot medications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps people get the right medicine by using special lists of medical terms called ontologies. Most medicine recommendation models learn from patient records, but some medicines don’t appear often in these records. This makes it hard for the models to learn about these medicines. The authors came up with a new way to use ontologies to help medication recommendation models learn more about all medicines. |
