A high throughput semantic concept frequency based approach for patient identification: a case study using type 2 diabetes mellitus clinical notes.

By Wei-Qi Wei, C. Tao, Guoqian Jiang, Christopher G. Chute. 2010

33 citations recorded by the source corpus.

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Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “In the reviewed papers, SVMs constantly outperform other learning algorithms for computational phenotyping [27], [89], [99], [118], [127].”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, methodology): “Two support vector machines (SVMs) were combined to automatically identify obesity types by extracting obesity and diabetes-related concepts from clinical text [86] in addition to patient identification [92].”
Limestone: High-throughput candidate phenotype generation via tensor factorization (2014, background): “…covering a variety of inter-related aspects of patients such as diagnoses, medication orders, and laboratory test ﬁndings [17,18]; sparsely sampled medical event sequences with different time scales across patients [19–21]; and noisy, incomplete, and inaccurate representation of patients [22,23].”
Identifying and Mitigating Biases in EHR Laboratory Tests (2014, unknown): “The work presented in this paper is highly relevant to researchers working on cohort identification algorithms, especially with the recent push for more automated ways to perform high-throughput phenotyping [34–36].”
Extracting research-quality phenotypes from electronic health records to support precision medicine (2015, background): “EHR data are highly complex and include both structured and unstructured information that must be woven together to create a phenotype algorithm [109,115].”