Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text

By Yuan Luo, Yu Xin, Ephraim P. Hochberg, Rohit Joshi, Ozlem Uzuner, Peter Szolovits. 2015

56 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mathematics, Natural Language Processing, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fei Wang
2. Anita M. Preininger
3. Xiaoqian Jiang
4. Yuan Luo
5. Liang Yao
6. Chengsheng Mao
7. Zexian Zeng
8. Hwanjo Yu

In Computational Mathematics

1. Xiaoqian Jiang
2. Hwanjo Yu
3. Yejin Kim
4. Fei Wang
5. Yuan Luo
6. Zexian Zeng
7. Jimeng Sun
8. Xiaoyu Li

In Natural Language Processing

1. Liang Yao
2. Chengsheng Mao
3. Fei Wang
4. Yuan Luo
5. Hwanjo Yu
6. Jimeng Sun
7. Irena Spasic
8. Goran Nenadic

In Medical & Health AI

1. Liang Yao
2. Chengsheng Mao
3. Fei Wang
4. Zexian Zeng
5. Tristan Naumann
6. Xiaoyu Li
7. Yu Deng
8. Yuan Luo

Citations in the corpus

• Graph Convolutional Networks for Text Classification (2018, background): “There are also existing studies on converting texts to graphs and perform feature engineering on graphs and sub-graphs (Luo, Uzuner, and Szolovits 2016; Rousseau, Kia-gias, and Vazirgiannis 2015; Skianis, Rousseau, and Vazir-giannis 2016; Luo et al. 2014; Luo et al. 2015).”
• Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “[106], [142], [178], [179], applied topic models and extended tensor-based topic models to learn better coherent features.”
• AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “[40] proposed to represent high-order semantic features from clinical texts as graphs and developed a subgraph-augmented nonnegative tensor factorization approach to analyze them.”
• Clinical Text Data in Machine Learning: Systematic Review (2020, methodology): “Clustering was used to identify latent groups of lymphoma patients from their pathology reports [66].”
• Predicting ICU Mortality Risk by Grouping Temporal Trends from a Multivariate Panel of Physiologic Measurements (2016, unknown): “Frequent subgraph mining (FSM) is a technique for pattern mining and has seen applications in natural language processing (Luo et al. 2014; Luo et al. 2015; Liu et al. 2013).”
• Discriminative and Distinct Phenotyping by Constrained Tensor Factorization (2017, methodology): “Recently, phenotyping based on Tucker model has been proposed 26, 28 .”