Abstract:
: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.
journal_name
Sci Advjournal_title
Science advancesauthors
Servadio JL,Convertino Mdoi
10.1126/sciadv.1701088subject
Has Abstractpub_date
2018-02-02 00:00:00pages
e1701088issue
2issn
2375-2548pii
1701088journal_volume
4pub_type
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