A better performing algorithm for identification of implausible growth data from longitudinal pediatric medical records-Reference-Cited by-同舟云学术

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A better performing algorithm for identification of implausible growth data from longitudinal pediatric medical records

Published:2024-08-06 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Harrall Kylie K.,Bird Sarah M.,Muller Keith E.,Vanderlinden Lauren A.,Payton Maya E.,Bellatorre Anna,Dabelea Dana,Glueck Deborah H.

Funder

National Institute of General Medical Sciences

National Institutes of Health

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-69161-5.pdf

Reference36 articles.

1. Daymont, C. et al. Automated identification of implausible values in growth data from pediatric electronic health records. J. Am. Med. Inform. Assoc. 24, 1080–1087 (2017).

2. Shi, J., Korsiak, J. & Roth, D. E. New approach for the identification of implausible values and outliers in longitudinal childhood anthropometric data. Ann. Epidemiol. 28, 204-211.e3 (2018).

3. Phan, H. T. T. et al. Automated data cleaning of paediatric anthropometric data from longitudinal electronic health records: Protocol and application to a large patient cohort. Sci. Rep. 10, 10164 (2020).

4. Lawman, H. G. et al. Comparing methods for identifying biologically implausible values in height, weight, and body mass index among youth. Am. J. Epidemiol. 182, 359–365 (2015).

5. Centers for Disease Control. Growth Charts - 2000 CDC Growth Charts - United States. https://www.cdc.gov/growthcharts/cdc_charts.htm (2022).

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