Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases.

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Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[34] implemented a heuristic rule-based”
Mining Electronic Health Records: A Survey (2017, methodology): “…et al. 2012; Pathak et al. 2012; Nadkarni et al. 2014; Chen et al. 2015; Chen et al. 2016; Hripcsak et al. 1997; Hripcsak and Albers 2013] Time Agnostic Case-Control or Cohort [Carroll et al. 2011; Xu et al. 2011a; Sarvestani et al. 2010] Time Aware Case-Control or Cohort [Albers et al. 2014; ?”
Detection of drug‐drug interactions through data mining studies using clinical sources, scientific literature and social media (2018, unknown): “There are some examples that showed that coded data may be insufficient to describe some steps in pharmacovigilance procedures and that the combination with narrative data can yield better results, such as building patient cohorts [55, 56].”
Electronic medical record phenotyping using the anchor and learn framework (2016, background): “Previous work has shown success in estimating phenotypes from raw data (e.g., smoking status, 20,21 rheumatoid arthritis, 22 and colorectal cancer 23 ), but the methodology for developing these predictors invariably uses manually labeled cases and controls derived from chart review.”