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Found 7 papers, 3 papers with code
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications
Electronic Health Record (EHR) datasets from Intensive Care Units (ICU) contain a diverse set of data modalities.
Dynamic Survival Analysis for Early Event Prediction
This study advances Early Event Prediction (EEP) in healthcare through Dynamic Survival Analysis (DSA), offering a novel approach by integrating risk localization into alarm policies to enhance clinical event metrics.
Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs
The rapid expansion of genomic sequence data calls for new methods to achieve robust sequence representations.
On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series
Recent findings in deep learning for tabular data are now surpassing these classical methods by better handling the severe heterogeneity of data input features.
Knowledge Graph Representations to enhance Intensive Care Time-Series Predictions
Intensive Care Units (ICU) require comprehensive patient data integration for enhanced clinical outcome predictions, crucial for assessing patient conditions.
Language Model Training Paradigms for Clinical Feature Embeddings
In research areas with scarce data, representation learning plays a significant role.
Multi-modal Graph Learning over UMLS Knowledge Graphs
The results demonstrate the significance of multi-modal medical concept representations based on prior medical knowledge.
