A model-based approach to Bayesian classification with applications to predicting pregnancy outcomes from longitudinal beta-hCG profiles.
Biostatistics
; 8(2): 228-38, 2007 Apr.
Article
en En
| MEDLINE
| ID: mdl-16754632
ABSTRACT
This paper discusses Bayesian statistical methods for the classification of observations into two or more groups based on hierarchical models for nonlinear longitudinal profiles. Parameter estimation for a discriminant model that classifies individuals into distinct predefined groups or populations uses appropriate posterior simulation schemes. The methods are illustrated with data from a study involving 173 pregnant women. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from beta human chorionic gonadotropin data available at early stages of pregnancy.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Embarazo
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Resultado del Embarazo
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Modelos Estadísticos
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Teorema de Bayes
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Gonadotropina Coriónica
Tipo de estudio:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
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Systematic_reviews
Límite:
Female
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Humans
Idioma:
En
Revista:
Biostatistics
Año:
2007
Tipo del documento:
Article
País de afiliación:
Chile