Cleaning of anthropometric data from PCORnet electronic health records using automated algorithms.
Cleaning of anthropometric data from PCORnet electronic health records using automated algorithms.
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作者：
展示growthcleanr的实用性，这是一种为电子健康记录（EHR）设计的人体测量数据清理方法。我们使用了一项正在进行的观察性研究中的所有可用的儿科和成人身高和体重数据，该研究包括来自15个医疗保健系统的EHR数据，并应用growthcleanr识别离群值和错误，并将其在儿科数据中的性能与其他2种儿科数据清理方法进行了比较：（1）条件百分位数（cp）和（2）儿科人体测量离群值标记管道（peanof）。687226名儿童（<20岁）和3267293名成人提供了71246369个体重和51525487个身高测量值。growthcleanr标记了18%的儿科和12%的成人测量结果，主要是作为儿科数据的结转测量值以及成人和儿科数据的重复测量值。根据CDC和其他既定临界点，删除标记的测量值后，分别有0.5%和0.6%的儿科身高和体重以及0.3%和1.4%的成人身高和体重在生物学上不可信。与其他儿科清洁方法相比，growthcleanr标记了大多数排除的测量值;但是，它没有标记一些更极端的测量值。在使用growthcleanr、cp和peanof进行清洁后，严重儿童肥胖的患病率分别为9.0%、9.2%和8.0%。 growthcleanr用于清理儿童和成人的身高和体重数据。它是唯一能够清理成人数据并识别结转和重复的方法，这在EHR中很普遍。研究结果可用于改进growthcleanr算法。
To demonstrate the utility of growthcleanr, an anthropometric data cleaning method designed for electronic health records (EHR). We used all available pediatric and adult height and weight data from an ongoing observational study that includes EHR data from 15 healthcare systems and applied growthcleanr to identify outliers and errors and compared its performance in pediatric data with 2 other pediatric data cleaning methods: (1) conditional percentile (cp) and (2) PaEdiatric ANthropometric measurement Outlier Flagging pipeline (peanof). 687 226 children (<20 years) and 3 267 293 adults contributed 71 246 369 weight and 51 525 487 height measurements. growthcleanr flagged 18% of pediatric and 12% of adult measurements for exclusion, mostly as carried-forward measures for pediatric data and duplicates for adult and pediatric data. After removing the flagged measurements, 0.5% and 0.6% of the pediatric heights and weights and 0.3% and 1.4% of the adult heights and weights, respectively, were biologically implausible according to the CDC and other established cut points. Compared with other pediatric cleaning methods, growthcleanr flagged the most measurements for exclusion; however, it did not flag some more extreme measurements. The prevalence of severe pediatric obesity was 9.0%, 9.2%, and 8.0% after cleaning by growthcleanr, cp, and peanof, respectively. growthcleanr is useful for cleaning pediatric and adult height and weight data. It is the only method with the ability to clean adult data and identify carried-forward and duplicates, which are prevalent in EHR. Findings of this study can be used to improve the growthcleanr algorithm.
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DOI：
10.1002/oby.21782
发表时间：
2017-04
期刊：
Obesity (Silver Spring, Md.)
影响因子：
--
作者：
Freedman DS;Butte NF;Taveras EM;Lundeen EA;Blanck HM;Goodman AB;Ogden CL
通讯作者：
Ogden CL
影响因子：
3.5
作者：
Shi X;Prins C;Van Pottelbergh G;Mamouris P;Vaes B;De Moor B
通讯作者：
De Moor B
DOI：
10.5334/egems.199
发表时间：
2018-04-13
期刊：
EGEMS (Washington, DC)
影响因子：
--
作者：
Qualls LG;Phillips TA;Hammill BG;Topping J;Louzao DM;Brown JS;Curtis LH;Marsolo K
通讯作者：
Marsolo K
影响因子：
120.7
作者：
Gerber, Jeffrey S.;Bryan, Matthew;Zaoutis, Theoklis E.
通讯作者：
Zaoutis, Theoklis E.
影响因子：
4.6
作者：
Phan, Hang T. T.;Borca, Florina;Ennis, Sarah
通讯作者：
Ennis, Sarah