Data mining of pediatric reference intervals

Laboratory tests are essential to assess the health status and to guide patient care in individuals of all ages. The interpretation of quantitative test results requires availability of appropriate reference intervals, and reference intervals in children have to account for the extensive physiologic...

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Autores principales:	Zierk Jakob, Metzler Markus, Rauh Manfred
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	data mining indirect methods pediatric reference intervals Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/dc133ceb14a7475694932348f3b02caf
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id	oai:doaj.org-article:dc133ceb14a7475694932348f3b02caf
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spelling	oai:doaj.org-article:dc133ceb14a7475694932348f3b02caf2021-12-05T14:10:52ZData mining of pediatric reference intervals2567-94302567-944910.1515/labmed-2021-0120https://doaj.org/article/dc133ceb14a7475694932348f3b02caf2021-12-01T00:00:00Zhttps://doi.org/10.1515/labmed-2021-0120https://doaj.org/toc/2567-9430https://doaj.org/toc/2567-9449Laboratory tests are essential to assess the health status and to guide patient care in individuals of all ages. The interpretation of quantitative test results requires availability of appropriate reference intervals, and reference intervals in children have to account for the extensive physiological dynamics with age in many biomarkers. Creation of reference intervals using conventional approaches requires the sampling of healthy individuals, which is opposed by ethical and practical considerations in children, due to the need for a large number of blood samples from healthy children of all ages, including neonates and young infants. This limits the availability and quality of pediatric reference intervals, and ultimately negatively impacts pediatric clinical decision-making. Data mining approaches use laboratory test results and clinical information from hospital information systems to create reference intervals. The extensive number of available test results from laboratory information systems and advanced statistical methods enable the creation of pediatric reference intervals with an unprecedented age-related accuracy for children of all ages. Ongoing developments regarding the availability and standardization of electronic medical records and of indirect statistical methods will further improve the benefit of data mining for pediatric reference intervals.Zierk JakobMetzler MarkusRauh ManfredDe Gruyterarticledata miningindirect methodspediatric reference intervalsMedical technologyR855-855.5ENJournal of Laboratory Medicine, Vol 45, Iss 6, Pp 311-317 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data mining indirect methods pediatric reference intervals Medical technology R855-855.5
spellingShingle	data mining indirect methods pediatric reference intervals Medical technology R855-855.5 Zierk Jakob Metzler Markus Rauh Manfred Data mining of pediatric reference intervals
description	Laboratory tests are essential to assess the health status and to guide patient care in individuals of all ages. The interpretation of quantitative test results requires availability of appropriate reference intervals, and reference intervals in children have to account for the extensive physiological dynamics with age in many biomarkers. Creation of reference intervals using conventional approaches requires the sampling of healthy individuals, which is opposed by ethical and practical considerations in children, due to the need for a large number of blood samples from healthy children of all ages, including neonates and young infants. This limits the availability and quality of pediatric reference intervals, and ultimately negatively impacts pediatric clinical decision-making. Data mining approaches use laboratory test results and clinical information from hospital information systems to create reference intervals. The extensive number of available test results from laboratory information systems and advanced statistical methods enable the creation of pediatric reference intervals with an unprecedented age-related accuracy for children of all ages. Ongoing developments regarding the availability and standardization of electronic medical records and of indirect statistical methods will further improve the benefit of data mining for pediatric reference intervals.
format	article
author	Zierk Jakob Metzler Markus Rauh Manfred
author_facet	Zierk Jakob Metzler Markus Rauh Manfred
author_sort	Zierk Jakob
title	Data mining of pediatric reference intervals
title_short	Data mining of pediatric reference intervals
title_full	Data mining of pediatric reference intervals
title_fullStr	Data mining of pediatric reference intervals
title_full_unstemmed	Data mining of pediatric reference intervals
title_sort	data mining of pediatric reference intervals
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/dc133ceb14a7475694932348f3b02caf
work_keys_str_mv	AT zierkjakob dataminingofpediatricreferenceintervals AT metzlermarkus dataminingofpediatricreferenceintervals AT rauhmanfred dataminingofpediatricreferenceintervals
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Data mining of pediatric reference intervals

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