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Found 4 papers, 1 papers with code
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.
Knowledge-Driven New Drug Recommendation
However, newly approved drugs do not have much historical prescription data and cannot leverage existing drug recommendation methods.
AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment
To tackle this problem, we propose AutoMap to automatically map the medical codes across different EHR systems in a coarse-to-fine manner: (1) Ontology-level Alignment: We leverage the ontology structure to learn a coarse alignment between the source and target medical coding systems; (2) Code-level Refinement: We refine the alignment at a fine-grained code level for the downstream tasks using a teacher-student framework.
Learning to Transfer via Modelling Multi-level Task Dependency
Besides, the understanding of relationships among tasks has been ignored by most of the current methods.
