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Development of composite indicators for integrated health in populations typically relies on a priori assumptions rather than model-free, data-driven evidence. Traditional variable selection processes tend not to consider relatedness and redundancy among variables, instead considering only individual correlations. In addition, a unified method for assessing integrated health statuses of populations is lacking, making systematic comparison among populations impossible. We propose the use of maximum entropy networks (MENets) that use transfer entropy to assess interrelatedness among selected variables considered for inclusion in a composite indicator. We also define optimal information networks (OINs) that are scale-invariant MENets, which use the information in constructed networks for optimal decision-making. Health outcome data from multiple cities in the United States are applied to this method to create a systemic health indicator, representing integrated health in a city.

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Hokkaido University Collection of Scholarly and Academic Papers  


Servadio, Joseph L. -  Convertino, Matteo - 

Id.: 70986148

Idioma: inglés  - 

Versión: 1.0

Estado: Final

Palabras clave547 - 

Tipo de recurso: article  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: https://creativecommons.org/licenses/by-nc/4.0/

Requerimientos técnicos:  Browser: Any - 

Fecha de contribución: 10-feb-2018


* Science Advances, 4(2): e1701088

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