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.<\/div>\n<\/section>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
We will discuss about \u201cOptimal information networks: Application for data-driven integrated health in populations\u201d, Servadio, Joseph L., and Matteo Convertino, Science Advances\u00a04.2 (2018): e1701088. Abstract Development of composite indicators for … <\/p>\n
Continue reading “Seokjoo Chae, Optimal information networks: Application for data-driven integrated health in populations”<\/span><\/a><\/p>\n","protected":false},"author":4,"featured_media":0,"template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[219],"class_list":["post-7180","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-journal-club","cat_journal-club"],"yoast_head":"\nSeokjoo Chae, Optimal information networks: Application for data-driven integrated health in populations - Biomedical Mathematics Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/2023-01-27-jc\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Seokjoo Chae, Optimal information networks: Application for data-driven integrated health in populations - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"We will discuss about \u201cOptimal information networks: Application for data-driven integrated health in populations\u201d, Servadio, Joseph L., and Matteo Convertino, Science Advances\u00a04.2 (2018): e1701088. 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