Electronic medical records for clinical research: application to the identification of heart failure.

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Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “applied Naive Bayes to predict heart failure [91], using coded data (e.”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, unknown): “using clinical notes only [40].”
Data from clinical notes: a perspective on the tension between structure and flexible documentation (2011, unknown): “and automatically calculate the Charlson comorbidity index.”
Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research (2008, methodology): “adapted the text analysis system created at the Mayo Clinic for the structuring of semi-structured notes for use in identifying patients with angina [142,143] and heart failure [144].”
Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts (2011, background): “…database knowledge discovery and association mining [16,17,18], identifying and classifying specific medical cases or conditions in an EPR [19,20,21,22], patient safety and automated surveillance of adverse events, contraindications and epidemics [23,24,25], comorbidity and disease networks…”