Subgraph Augmented Non-Negative Tensor Factorization (SANTF) for Modeling Clinical Narrative Text
Conclusion SANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features.
Source: Journal of the American Medical Informatics Association - Category: Information Technology Authors: Luo, Y., Xin, Y., Hochberg, E., Joshi, R., Uzuner, O., Szolovits, P. Tags: Research and Applications Source Type: research