Automated detection of poor-quality data: case studies in healthcare Article Swipe

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M. Abou Dakka , Tuc Nguyen , Jonathan M. M. Hall , Sonya M. Diakiw , Matthew VerMilyea , Rebecca Linke , Michelle Perugini , Don Perugini ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1038/s41598-021-97341-0

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Generalizability theory Computer science Raw data Triage Data quality Data cleansing Quality (philosophy) Health care Data mining Identification (biology) Data science Artificial intelligence Risk analysis (engineering) Metric (unit) Medicine Medical emergency Operations management Botany Programming language Economics Philosophy Mathematics Statistics Biology Epistemology Economic growth

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Type: article
Language: en
Landing Page: https://doi.org/10.1038/s41598-021-97341-0
PDF: https://www.nature.com/articles/s41598-021-97341-0.pdf
OA Status: gold
Cited By: 29
References: 28
Related Works: 10
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DOI: https://doi.org/10.1038/s41598-021-97341-0

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Title: Automated detection of poor-quality data: case studies in healthcare

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Type: article

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Language: en

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Publication year: 2021

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Publication date: 2021-09-09

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Authors: M. Abou Dakka, Tuc Nguyen, Jonathan M. M. Hall, Sonya M. Diakiw, Matthew VerMilyea, Rebecca Linke, Michelle Perugini, Don Perugini

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Landing page: https://doi.org/10.1038/s41598-021-97341-0

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PDF URL: https://www.nature.com/articles/s41598-021-97341-0.pdf

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Concepts: Generalizability theory, Computer science, Raw data, Triage, Data quality, Data cleansing, Quality (philosophy), Health care, Data mining, Identification (biology), Data science, Artificial intelligence, Risk analysis (engineering), Metric (unit), Medicine, Medical emergency, Operations management, Botany, Programming language, Economics, Philosophy, Mathematics, Statistics, Biology, Epistemology, Economic growth

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Cited by: 29

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