Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.

By Robert J. Carroll, A. Eyler, Joshua Charles Denny. 2011

122 citations recorded by the source corpus.

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Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “[27] implemented a SVM model for rheumatoid arthritis identification using a set of features from clinical narratives using the Knowledge Map Concept Identifier (KMCI) [126].”
• Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data (2013, methodology): “Some progress has been made using supervised machine learning to define disease-level cohorts [32,33], which avoids the manual phenotype specification but still includes all of the other effort associated with supervised methods.”
• Applying active learning to high-throughput phenotyping algorithms for electronic health records data. (2013, methodology): “In another study, Carroll et al11 also demonstrated that the ML-based phenotyping method has good portability to identify RA from three EHR systems.”
• Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources (2015, methodology): “41 Carroll et al. 42 used all possible ICD-9 codes, UMLS concepts, medication mentions, and additional features with frequency-based screening to classify rheumatoid arthritis (RA) using SVM.”
• A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases (2015, methodology): “NLP-informed machine learning algorithms have been shown to be successful in identifying patients with rheumatoid arthritis (Carroll, Eyler, & Denny, 2011; Chen et al., 2013), diabetes (Wright, McCoy, Henkin, Kale, & Sittig, 2013), colorectal cancer, and venous thromboembolism (Chen et al., 2013),…”
• L G ] 1 1 A pr 2 01 7 Federated Tensor Factorization for Computational Phenotyping (2018, methodology): “Current approaches for translating EHR data into useful phenotypes are typically slow, manually intensive and limited in scope [4, 5].”
• Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, methodology): “Data mining and machine learning tools have been utilized to automate the phenotype generation process [24,36,37,26].”