J Biomed Inform - Sparse modeling of spatial environmental variables associated with asthma.

Tópicos

{ spatial(1525) area(1432) region(1030) }
{ model(2341) predict(2261) use(1141) }
{ risk(3053) factor(974) diseas(938) }
{ analysi(2126) use(1163) compon(1037) }
{ imag(1947) propos(1133) code(1026) }
{ ehr(2073) health(1662) electron(1139) }
{ group(2977) signific(1463) compar(1072) }
{ patient(2837) hospit(1953) medic(668) }
{ patient(1821) servic(1111) care(1106) }
{ care(1570) inform(1187) nurs(1089) }
{ data(1737) use(1416) pattern(1282) }
{ imag(2675) segment(2577) method(1081) }
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{ research(1218) medic(880) student(794) }
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{ search(2224) databas(1162) retriev(909) }
{ studi(1410) differ(1259) use(1210) }
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{ cost(1906) reduc(1198) effect(832) }
{ gene(2352) biolog(1181) express(1162) }
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{ first(2504) two(1366) second(1323) }
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Resumo

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.

Resumo Limpo

geograph distribut environment factor influenc burden diseas asthma object identifi spars environment variabl associ asthma diagnosi gather larg electron health record ehr dataset control spatial variat ehr dataset univers wisconsin famili medicin intern medicin pediatr depart obtain patient age year threeyear period patient home address geocod one geograph census block group one thousand block group variabl obtain commerci databas develop spars spatial environment analysi sasea use method environment variabl first dimension reduc spars princip compon analysi logist thin plate regress spline model use identifi block group variabl associ asthma spars princip compon address patient ehr dataset distribut throughout major wisconsin geographi logist thin plate regress spline model captur spatial variat asthma four spars princip compon identifi via model select consist food home dog ownership household size dispos incom variabl rural area dog ownership renter occupi hous unit signific spars princip compon associ asthma main contribut incorpor sparsiti spatial model sasea sequenti ad spars princip compon logist thin plate regress spline model method allow associ geograph distribut environment factor asthma use ehr environment dataset sasea can appli diseas environment risk factor

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