{"id":27898,"date":"2015-03-01T00:00:00","date_gmt":"2018-12-03T21:59:24","guid":{"rendered":"https:\/\/aprifel-pp.mentalworks.biz\/fr\/resume-scientifique\/web-search-activity-data-accurately-predict-population-chronic\/"},"modified":"2018-12-03T22:59:24","modified_gmt":"2018-12-03T21:59:24","slug":"web-search-activity-data-accurately-predict-population-chronic","status":"publish","type":"resume","link":"https:\/\/aprifel-pp.mentalworks.biz\/fr\/resume-scientifique\/web-search-activity-data-accurately-predict-population-chronic\/","title":{"rendered":"Web search activity data accurately predict population chronic disease risk in the USA."},"content":{"rendered":"

BACKGROUND<\/strong>
\nThe WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors.<\/p>\n

METHODS<\/strong>
\nWeb activity output for each element of the WHO's Causes of NCD framework was used as a basis for identifying relevant web search activity from 2004 to 2013 for the USA. Multiple linear regression models with regularisation were used to generate predictive algorithms, mapping web search activity to Centers for Disease Control and Prevention (CDC) measured risk factor\/disease prevalence. Predictions for subsequent target years not included in the model derivation were tested against CDC data from population surveys using Pearson correlation and Spearman's r.<\/p>\n

RESULTS<\/strong>
\nFor 2011 and 2012, predicted prevalence was very strongly correlated with measured risk data ranging from fruits and vegetables consumed (r=0.81; 95% CI 0.68 to 0.89) to alcohol consumption (r=0.96; 95% CI 0.93 to 0.98). Mean difference between predicted and measured differences by State ranged from 0.03 to 2.16. Spearman's r for state-wise predicted versus measured prevalence varied from 0.82 to 0.93.<\/p>\n

CONCLUSIONS<\/strong>
\nThe high predictive validity of web search activity for NCD risk has potential to provide real-time information on population risk during policy implementation and other population-level NCD prevention efforts.<\/p>\n","protected":false},"template":"","mots_cles":[],"class_list":["post-27898","resume","type-resume","status-publish","hentry"],"acf":{"adresse":"Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Victoria, Australia.\r\nshivanand85@gmail.com","annee":"2015","mois":"3","numero":"69:7","page":"693-9","auteurs":[{"ID":27892,"post_author":"0","post_date":"2018-12-03 22:59:20","post_date_gmt":"2018-12-03 21:59:20","post_content":"","post_title":"Thin Nguyen","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"thin-nguyen","to_ping":"","pinged":"","post_modified":"2018-12-03 22:59:20","post_modified_gmt":"2018-12-03 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