Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

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Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[28] collected concepts from publicly available knowledge sources (e.”
Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression (2017, background): “14 In recent years, mental health researchers in South London and Maudsley NHS Trust have begun using EMRs for research recruitment.”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, background): “Recent work demonstrated the success of extracting semantic relations and using these relations as additional features in diagnosis categorization, some through better grouping features using curated relations [48], others through unsupervised learning to extract more expressive representation of re”
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability (2016, background): “Nonstandard features including customized access controls, phenotype workflow, and integrated external code for data validation features, described in more detail below, were implemented with custom programming using Drupal’s standard application programming interface.”