A New Model

Blatt, Amy J.

doi:10.1007/978-3-319-12003-4_10

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Geographicknowledgediscoveryisdefinedasthe"processofextractinginformationandknowledge frommassivegeo-referenceddatabases" (Miller&Han,2009).Aspatialdataminingprocessbasedon adatabasemanagementsystemincludesdatabaseinterfacesbytakingcareofspatialdatastructures andqueryoptimization,thenfocusingonobjectsandattributesextractions,thendeployingpattern extractiontechniquessuchasstatistical,machinelearningandotherdataminingtechniques,then evaluating interestingness and statistical significance, and eventually achieving discoveries. Blatt (2014)studiesafewareasofgeospatialdataminingincludingdataminingalgorithmsinmedical geographic research, use of geospatial visualization, and the growth of geospatial data mining applicationsinpublichealth.Inknowledgediscoveryandreportingprocesses,geo-visualizationis usedtoexploreandanalyzegeospatialdata,andtoimplementvisualtoolsforknowledgeextraction, synthesis,communication (Guo&Mennis,2009).…”

Section: Geographic Data Mining and Knowledge Discoverymentioning

confidence: 99%

Data Mining Problems Classification and Techniques

Rahman

2018

International Journal of Big Data and Analytics in Healthcare

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Data mining techniques are widely used to uncover hidden knowledge that cannot be extracted using conventional information retrieval and data analytics tools or using any manual techniques. Different data mining techniques have evolved over the last two decades and solve a wide variety of business problems. Different techniques have been proposed. Practitioners and researchers in both industry and academia continuously develop and experiment with variety of data mining techniques. This article provides a consolidated list of problems being solved by different data mining techniques. The author presents up to three techniques that can be used to address a particular type of problem. The objective is to assist practitioners and researchers to have a holistic view of data mining techniques, and the problems being solved by them. This article also provides an overview of data mining problems solved in the healthcare industry. The article also highlights as to how big data technologies are leveraged in handling and processing huge amounts of complex data from data mining perspectives.

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Section: Geographic Data Mining and Knowledge Discoverymentioning

confidence: 99%

Data Mining Problems Classification and Techniques

Rahman

2018

International Journal of Big Data and Analytics in Healthcare

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Spatial Patterns of Epilepsy-Related Emergency Department Visits in California

Banta

Addison

Beeson

2015

Journal of Public Health Research

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BackgroundSocio-demographic factors are associated with increased emergency department (ED) use among patients with epilepsy. However, there has been limited spatial analysis of such visits.Design and methodsCalifornia ED visit at the patient ZIP Code level were examined using Kulldorf’s spatial scan statistic to identify clusters of increased risk for epilepsy-related visits. Logistic regression was used to examine the relative importance of patient socio-demographics, Census-based and hospital measures.ResultsDuring 2009-2011 there were 29,715,009 ED visits at 330 hospitals, of which 139,235 (0.5%) had epilepsy (International Classification of Disease-9 345.xx) as the primary diagnosis. Three large urban clusters of high epilepsy-related ED visits were centred in the cities of Los Angeles, Oakland and Stockton and a large rural cluster centred in Kern County. No consistent pattern by age, race/ethnicity, household structure, and income was observed among all clusters. Regression found only the Los Angeles cluster significant after adjusting for other measures.ConclusionsGeospatial analysis within a large and geographically diverse region identified a cluster within its most populous city having an increased risk of ED visits for epilepsy independent of selected socio-demographic and hospital measures. Additional research is necessary to determine whether elevated rates of ED visits represent increased prevalence of epilepsy or an inequitable system of epilepsy care.Significance for public healthThere have been few spatial analyses regarding treatment for epilepsy. This paper significantly expands upon previous work by simultaneously considering multiple urban centres and sparsely populated agricultural and desert/mountain areas in a large state. Furthermore, most epilepsy studies involve one system of care or funding source (such as Department of Veterans Affairs, Medicare, Medicaid, or private insurance plans). This paper considers all funding sources at community-based hospitals. Patient socio-demographics, area-based summaries of socio-demographics, and basic hospital characteristics explain most of the observed spatial variation in rates of emergency department (ED) visits related to epilepsy. However, preliminary spatial analysis demonstrated that an area within downtown Los Angeles did have a higher rate of epilepsy-related visits compared to the rest of the state. A more comprehensive surveillance approach with ED visit data could be readily applied to other large geographic areas and be useful both for on-going monitoring and public health intervention

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A New Model

Cited by 2 publications

References 21 publications

Data Mining Problems Classification and Techniques

Data Mining Problems Classification and Techniques

Spatial Patterns of Epilepsy-Related Emergency Department Visits in California

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