Information Visualization for Chronic Disease Risk Assessment
Here, the authors describe and evaluate a new information-visualization method and prototype software tool that support risk assessment for negative health outcomes. Their framework uses principal component analysis and linear discriminant analysis to plot high-dimensional patient data in 2D. It als...
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| Veröffentlicht in: | IEEE intelligent systems 2012-11, Vol.27 (6), p.81-85 |
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| container_title | IEEE intelligent systems |
| container_volume | 27 |
| creator | Harle, C. A. Neill, D. B. Padman, R. |
| description | Here, the authors describe and evaluate a new information-visualization method and prototype software tool that support risk assessment for negative health outcomes. Their framework uses principal component analysis and linear discriminant analysis to plot high-dimensional patient data in 2D. It also incorporates interactive visualization techniques to aid the identification of high versus low risk patients, critical risk factors, and the estimated effect of hypothetical interventions on the likelihood of negative outcomes. The authors quantitatively evaluated the visualization method using a secondary dataset describing 588 people with diabetes and their estimated future risk of heart attack. Their results show that the method visually classifies high- and low-risk people with accuracy that's similar to other common statistical methods. The framework also provides an interactive, visualization-based tool for clinicians to explore the nuances of their patients' data and disease risk. |
| doi_str_mv | 10.1109/MIS.2012.112 |
| format | Article |
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A. ; Neill, D. B. ; Padman, R.</creator><creatorcontrib>Harle, C. A. ; Neill, D. B. ; Padman, R.</creatorcontrib><description>Here, the authors describe and evaluate a new information-visualization method and prototype software tool that support risk assessment for negative health outcomes. Their framework uses principal component analysis and linear discriminant analysis to plot high-dimensional patient data in 2D. It also incorporates interactive visualization techniques to aid the identification of high versus low risk patients, critical risk factors, and the estimated effect of hypothetical interventions on the likelihood of negative outcomes. The authors quantitatively evaluated the visualization method using a secondary dataset describing 588 people with diabetes and their estimated future risk of heart attack. Their results show that the method visually classifies high- and low-risk people with accuracy that's similar to other common statistical methods. The framework also provides an interactive, visualization-based tool for clinicians to explore the nuances of their patients' data and disease risk.</description><identifier>ISSN: 1541-1672</identifier><identifier>EISSN: 1941-1294</identifier><identifier>DOI: 10.1109/MIS.2012.112</identifier><identifier>CODEN: IISYF7</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Chronic illnesses ; Computer science; control theory; systems ; Datasets ; dimensionality reduction ; Discriminant analysis ; Diseases ; Exact sciences and technology ; healthcare ; Heart attacks ; Information technology ; information visualization ; Medical information processing ; Medical services ; Methods ; Pattern recognition. Digital image processing. 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A.</creatorcontrib><creatorcontrib>Neill, D. B.</creatorcontrib><creatorcontrib>Padman, R.</creatorcontrib><title>Information Visualization for Chronic Disease Risk Assessment</title><title>IEEE intelligent systems</title><addtitle>MIS</addtitle><description>Here, the authors describe and evaluate a new information-visualization method and prototype software tool that support risk assessment for negative health outcomes. Their framework uses principal component analysis and linear discriminant analysis to plot high-dimensional patient data in 2D. It also incorporates interactive visualization techniques to aid the identification of high versus low risk patients, critical risk factors, and the estimated effect of hypothetical interventions on the likelihood of negative outcomes. The authors quantitatively evaluated the visualization method using a secondary dataset describing 588 people with diabetes and their estimated future risk of heart attack. 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A.</au><au>Neill, D. B.</au><au>Padman, R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Information Visualization for Chronic Disease Risk Assessment</atitle><jtitle>IEEE intelligent systems</jtitle><stitle>MIS</stitle><date>2012-11-01</date><risdate>2012</risdate><volume>27</volume><issue>6</issue><spage>81</spage><epage>85</epage><pages>81-85</pages><issn>1541-1672</issn><eissn>1941-1294</eissn><coden>IISYF7</coden><abstract>Here, the authors describe and evaluate a new information-visualization method and prototype software tool that support risk assessment for negative health outcomes. Their framework uses principal component analysis and linear discriminant analysis to plot high-dimensional patient data in 2D. It also incorporates interactive visualization techniques to aid the identification of high versus low risk patients, critical risk factors, and the estimated effect of hypothetical interventions on the likelihood of negative outcomes. 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| issn | 1541-1672 1941-1294 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Artificial intelligence Chronic illnesses Computer science control theory systems Datasets dimensionality reduction Discriminant analysis Diseases Exact sciences and technology healthcare Heart attacks Information technology information visualization Medical information processing Medical services Methods Pattern recognition. Digital image processing. Computational geometry Risk assessment Risk factors Software development Statistical methods Visualization |
| title | Information Visualization for Chronic Disease Risk Assessment |
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