Automatic lymphoma classification with sentence subgraph mining from pathology reports.

By Yuan Luo, Aliyah R. Sohani, Ephraim P. Hochberg, Peter Szolovits. 2014

58 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Information Extraction, Medical & Health AI

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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, methodology): “criteria [195], representing test results for automating diagnosis categorization [108], and building pharmacogenomic semantic networks [58], where the number of nodes is flexi-”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, background): “A number of studies have focused on different applications of NLP in pathology, histopathology, and radiology reports [69], including extracting relevant domain entities from narrative cancer pathology reports [70], negation detection of medical entities in pathology reports [71], sentence translati”
Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction (2017, unknown): “Our preliminary data show that mining and grouping relation subgraphs lead to improved accuracy and better interpretability in diagnostic reasoning but call for a Bayesian formulation to incorporate existing medical knowledge, provide confidence estimation, and further improve prediction accuracy to”
Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, methodology): “However, there is a gap between the technical advances and the demands from many real-world tasks, including building pharmacogenomics semantic networks [35], extracting clinical trial eligibility criteria [32] and representing test results for automating diagnosis categorization [44,45].”
Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, methodology): “To this end, graph methods are a promising class of algorithms and should be actively investigated [14, 92].”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, methodology): “[49, 50, 92] Stanford (augmented by UMLS) Frequent subgraph mining with redundancy removing to extract complex relations”