Exploring the Application of Deep Learning Techniques on Medical Text Corpora

By José Antonio Miñarro-Giménez, Oscar Marín-Alonso, Matthias Samwald. 2014

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Patient outcome prediction via convolutional neural networks based on multi-granularity medical concept embedding (2017, methodology): “[18] developed a method to learn embedding from unstructured medical corpus crawled from PubMed, Merck Manuals, Medscape and Wikipedia.”
Multi-layer Representation Learning for Medical Concepts (2016, methodology): “[27] learns the representations of medical terms by applying Skip-gram to various medical text col-”
Using recurrent neural network models for early detection of heart failure onset (2016, methodology): “Deep learning techniques have been recently applied to clinical text data (eg, PubMed abstracts, progress notes) using Skip-gram 20,32,33 to learn relationships among clinical processes or unified medical language system (UMLS) concepts.”
Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature (2018, methodology): “[19] were the first to apply neural language models to PubMed corpora.”