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Paper Details

The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.
J Biomed Inform
17
2015
heart disease, participants, risk
Aged, Cohort Studies, Comorbidity, Computer Security, Confidentiality, Coronary Artery Disease, Data Mining, Diabetes Complications, Electronic Health Records, Female, Humans, Incidence, Longitudinal Studies, Male, Maryland, Middle Aged, Narration, Natural Language Processing, Pattern Recognition, Automated, Risk Assessment, Supervised Machine Learning, Vocabulary, Controlled
Author NameAffiliation
Kirk RobertsLister Hill National Center for Biomedical Communications, National Institutes of Health
Sonya E ShooshanLister Hill National Center for Biomedical Communications, National Institutes of Health
Laritza RodriguezLister Hill National Center for Biomedical Communications, National Institutes of Health
Swapna AbhyankarLister Hill National Center for Biomedical Communications, National Institutes of Health
Halil KilicogluLister Hill National Center for Biomedical Communications, National Institutes of Health
Dina Demner-FushmanLister Hill National Center for Biomedical Communications, National Institutes of Health
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