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Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records
Therefore, in this paper, we propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework that utilizes survival durations from both censored and observed data to define temporal distinctiveness and construct negative sample pairs with adjustable hardness for contrastive learning.
A Semantic Modular Framework for Events Topic Modeling in Social Media
The advancement of social media contributes to the growing amount of content they share frequently.
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