Novel Pediatric Height Outlier Detection Methodology for Electronic Health Records via Machine Learning With Monotonic Bayesian Additive Regression Trees.
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| Title: | Novel Pediatric Height Outlier Detection Methodology for Electronic Health Records via Machine Learning With Monotonic Bayesian Additive Regression Trees. |
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| Authors: | Sparapani RA; From the Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI., Teng BQ; From the Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI., Hilbrands J; the Clinical Nutrition, Children's Wisconsin, Milwaukee, WI., Pipkorn R; the Clinical Nutrition, Children's Wisconsin, Milwaukee, WI., Feuling MB; the Clinical Nutrition, Children's Wisconsin, Milwaukee, WI., Goday PS; the Pediatric Gastroenterology and Nutrition, Medical College of Wisconsin, Milwaukee, WI. |
| Source: | Journal of pediatric gastroenterology and nutrition [J Pediatr Gastroenterol Nutr] 2022 Aug 01; Vol. 75 (2), pp. 210-214. Date of Electronic Publication: 2022 Jun 01. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Wiley Country of Publication: United States NLM ID: 8211545 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1536-4801 (Electronic) Linking ISSN: 02772116 NLM ISO Abbreviation: J Pediatr Gastroenterol Nutr Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1536-4801 |
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| DOI: | 10.1097/MPG.0000000000003492 |
