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symptom-networks

topic1 events
papersTODAY 04:00 UTC

Scalable partial information decomposition for symptom networks via supervised embeddings

Researchers propose a scalable method for partial information decomposition (PID) that uses supervised embeddings to analyze mental-health symptom networks. Standard pairwise edge weights cannot capture redundant information shared about a third symptom or synergistic information that only emerges from combinations. The approach aims to make PID practical for larger symptom networks.

research/mental-health/embeddings/network-analysis