EHR phenotypes
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Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time- and labor-intensive.
10p visteverogers 24-06-2023 11 4 Download
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We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts.
8p visteverogers 24-06-2023 5 2 Download
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Electronic health records (EHRs) are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems, and collected for purposes other than medical research. We describe an approach for developing, validating, and sharing reproducible phenotypes from national structured EHR in the United Kingdom with applications for translational research.
15p visteverogers 24-06-2023 4 2 Download
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Accurate and efficient identification of complex chronic conditions in the electronic health record (EHR) is an important but challenging task that has historically relied on tedious clinician review and oversimplification of the disease.
5p visteverogers 24-06-2023 3 2 Download
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The use of electronic health records (EHRs) for research has the potential to improve the diagnosis and treatment of disease, yet contact with patients based on results of EHR phenotyping has received little attention.
8p visteverogers 24-06-2023 4 3 Download
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Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms.
7p visteverogers 24-06-2023 4 2 Download
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Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as 2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens.
7p visteverogers 24-06-2023 6 2 Download
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Despite increased risk for negative outcomes, cognitive impairment (CI) is greatly under-detected during hospitalization. While automated EHR-based phenotypes have potential to improve recognition of CI, they are hindered by widespread under-diagnosis of underlying etiologies such as dementia—limiting the utility of more precise structured data elements.
7p visteverogers 24-06-2023 3 2 Download
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This study proposes a novel Prior knowledge guided Integrated likelihood Estimation (PIE) method to correct bias in estimations of associations due to misclassification of electronic health record (EHR)-derived binary phenotypes, and evaluates the performance of the proposed method by comparing it to 2 methods in common practice.
8p visteverogers 24-06-2023 5 2 Download
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Developing algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to streamline the phenotyping process within EHRs.
13p vighostrider 25-05-2023 11 2 Download
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Multimodal automated phenotyping (MAP) is a scalable, high-throughput phenotyping method, developed using electronic health record (EHR) data from an adult population. We tested transportability of MAP to a pediatric population.
5p vighostrider 25-05-2023 2 2 Download
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The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect.
8p vighostrider 25-05-2023 4 2 Download
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To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects.
9p vighostrider 25-05-2023 5 2 Download
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The growth of DNA biobanks linked to data from electronic health records (EHRs) has enabled the discovery of numerous associations between genomic variants and clinical phenotypes.
7p viansan2711 30-07-2021 16 1 Download