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Optimal information networks : Application for data-driven integrated health in populations


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タイトル: Optimal information networks : Application for data-driven integrated health in populations
著者: Servadio, Joseph L. 著作を一覧する
Convertino, Matteo 著作を一覧する
発行日: 2018年 2月 2日
出版者: American Association for the Advancement of Science
誌名: Science Advances
巻: 4
号: 2
開始ページ: e1701088
出版社 DOI: 10.1126/sciadv.1701088
抄録: 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.
資料タイプ: article
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: Matteo Convertino


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