ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2018
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Etiology-guided mutational signature learning from DNA repair knockouts in cell lines using supervised NMF.NAR genomics and bioinformatics · 2026Article
- NMFProfiler: a multi-omics integration method for samples stratified in groups.Bioinformatics (Oxford, England) · 2025Article
- Longitudinal clustering of Life's Essential 8 health metrics: application of a novel unsupervised learning method in the CARDIA study.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis.Frontiers in oncology · 2022Article
- Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorization.Scientific reports · 2021Article
- A Novel Application of Non-Negative Matrix Factorization to the Prediction of the Health Status of Undocumented Immigrants.Health equity · 2021Article
- Phenotyping Multiple Organ Dysfunction Syndrome Using Temporal Trends in Critically Ill Children.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily; on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the interpretability and accuracy problems. Most existing methods lack of the interpretability, but recently Subgraph Augmented Nonnegative Matrix Factorization (SANMF) has been successfully applied to time series data to provide a path to interpret the features well. Therefore, we adopted this approach as the backbone to analyze the patient data. One limitation of the original SANMF method is its poor prediction ability due to its unsupervised nature. To deal with this problem, we proposed a supervised SANMF algorithm by integrating the logistic regression loss function into the NMF framework and solved it with an alternating optimization procedure. We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
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