Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals
By Pedro L. Teixeira, Wei-Qi Wei, Robert M Cronin, Huan Mo, Jacob P VanHouten, Robert J. Carroll, Eric R. LaRose, L. Bastarache, S. Rosenbloom, Todd L Edwards and 6 more. 2017
87 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Health Information Management
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Citations in the corpus
- Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[116] found random forests were superior to rule-based systems with a median AUC score of 98 percent when they were trying to identify hypertension using billing”
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, methodology): “Clinical notes and several types of medical documents were also used to identify hypertensive individuals using open-source medication information extraction (IE) system MedEx [49].”
- Remote patient monitoring using artificial intelligence: Current state, applications, and challenges (2023, background): “Random forests need relatively few training cases to achieve near-peak performance, are computationally cheap to train, and are able to handle large numbers of descriptors well (Teixeira et al., 2016).”
- Clinical Text Data in Machine Learning: Systematic Review (2020, background): “Most often, EHR phenotyping focused on a single medical condition, eg, axial spondyloarthritis [97], hypertension [89], systemic lupus erythematosus [95], dermatitis [44], obesity [61], celiac disease [22], epilepsy [41], autism [84], or psychiatric problems in general [40].”