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Latent Patient Network Learning for Automatic Diagnosis
[article]
2020
arXiv
pre-print
Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key component in these models is to build a population graph, where the graph adjacency matrix represents pair-wise patient similarities. Until now, the similarity metrics have been defined manually, usually based on meta-features like demographics or clinical scores. The definition of the metric, however, needs careful tuning, as GCNs are
arXiv:2003.13620v1
fatcat:cdfxbi5sezaobil4xuc4xrnqum