A rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.

<h4>Objective</h4>To identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction.<h4>Research design and methods</h4>RuleFit is used to identify the risk-predictive baseli...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ying Lin, Xiaoning Qian, Jeffrey Krischer, Kendra Vehik, Hye-Seung Lee, Shuai Huang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5156fdb58a494ee4b3911c582601325f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5156fdb58a494ee4b3911c582601325f
record_format	dspace
spelling	oai:doaj.org-article:5156fdb58a494ee4b3911c582601325f2021-11-18T08:15:38ZA rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.1932-620310.1371/journal.pone.0091095https://doaj.org/article/5156fdb58a494ee4b3911c582601325f2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24926781/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Objective</h4>To identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction.<h4>Research design and methods</h4>RuleFit is used to identify the risk-predictive baseline profile patterns of demographic, immunologic, and metabolic markers, using 356 subjects who were randomized into the control arm of the prospective Diabetes Prevention Trial-Type 1 (DPT-1) study. A novel latent trait model is developed to synthesize these baseline profile patterns for disease risk prediction. The primary outcome was Type 1 Diabetes (T1D) onset.<h4>Results</h4>We identified ten baseline profile patterns that were significantly predictive to the disease onset. Using these ten baseline profile patterns, a risk prediction model was built based on the latent trait model, which produced superior prediction performance over existing risk score models for T1D.<h4>Conclusion</h4>Our results demonstrated that the underlying disease progression process of T1D can be detected through some risk-predictive patterns of demographic, immunologic, and metabolic markers. A synthesis of these patterns provided accurate prediction of disease onset, leading to more cost-effective design of prevention trials of T1D in the future.Ying LinXiaoning QianJeffrey KrischerKendra VehikHye-Seung LeeShuai HuangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e91095 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ying Lin Xiaoning Qian Jeffrey Krischer Kendra Vehik Hye-Seung Lee Shuai Huang A rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.
description	<h4>Objective</h4>To identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction.<h4>Research design and methods</h4>RuleFit is used to identify the risk-predictive baseline profile patterns of demographic, immunologic, and metabolic markers, using 356 subjects who were randomized into the control arm of the prospective Diabetes Prevention Trial-Type 1 (DPT-1) study. A novel latent trait model is developed to synthesize these baseline profile patterns for disease risk prediction. The primary outcome was Type 1 Diabetes (T1D) onset.<h4>Results</h4>We identified ten baseline profile patterns that were significantly predictive to the disease onset. Using these ten baseline profile patterns, a risk prediction model was built based on the latent trait model, which produced superior prediction performance over existing risk score models for T1D.<h4>Conclusion</h4>Our results demonstrated that the underlying disease progression process of T1D can be detected through some risk-predictive patterns of demographic, immunologic, and metabolic markers. A synthesis of these patterns provided accurate prediction of disease onset, leading to more cost-effective design of prevention trials of T1D in the future.
format	article
author	Ying Lin Xiaoning Qian Jeffrey Krischer Kendra Vehik Hye-Seung Lee Shuai Huang
author_facet	Ying Lin Xiaoning Qian Jeffrey Krischer Kendra Vehik Hye-Seung Lee Shuai Huang
author_sort	Ying Lin
title	A rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.
title_short	A rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.
title_full	A rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.
title_fullStr	A rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.
title_full_unstemmed	A rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.
title_sort	rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/5156fdb58a494ee4b3911c582601325f
work_keys_str_mv	AT yinglin arulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT xiaoningqian arulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT jeffreykrischer arulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT kendravehik arulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT hyeseunglee arulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT shuaihuang arulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT yinglin rulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT xiaoningqian rulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT jeffreykrischer rulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT kendravehik rulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT hyeseunglee rulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns AT shuaihuang rulebasedprognosticmodelfortype1diabetesbyidentifyingandsynthesizingbaselineprofilepatterns
_version_	1718421983232786432

A rule-based prognostic model for type 1 diabetes by identifying and synthesizing baseline profile patterns.

Ejemplares similares