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Data Integration and Predictive Analysis System for Disease Prophylaxis

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Title: Data Integration and Predictive Analysis System for Disease Prophylaxis
Authors: Erraguntla, Madhav
Freeze, John
Delen, Dursun
Madanagopal, Karthic
Mayer, Ric
show 1 moreKhojasteh, Jam
show less
Keywords: Data integration
predictive analysis
disease prophylaxis
infectious diseases
influenza
Issue Date: 04 Jan 2017
Abstract: The goal of the Data Integration and Predictive Analysis System (IPAS) is to enable prediction, analysis, and response management for incidents of infectious diseases. IPAS collects and integrates comprehensive datasets of previous disease incidents and potential influencing factors to facilitate multivariate, predictive analytics of disease patterns, intensity, and timing. IPAS supports comprehensive epidemiological analysis - exploratory spatial and temporal correlation, hypothesis testing, prediction, and intervention analysis. Innovative machine learning and predictive analytical techniques like support vector machines (SVM), decision tree-based random forests, and boosting are used to predict the disease epidemic curves. Predictions are then displayed to stakeholders in a disease situation awareness interface, alongside disease incidents, syndromic and zoonotic details extracted from news sources and medical publications. Data on Influenza Like Illness (ILI) provided by CDC was used to validate the capability of IPAS system, with plans to expand to other illnesses in the future. This paper presents the ILI prediction modeling results as well as IPAS system features.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41287
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.134
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Data, Text, and Web Mining for Business Analytics Minitrack



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