BMC Med Inform Decis Mak - Spatial cluster detection using dynamic programming.

Tópicos

{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }
{ spatial(1525) area(1432) region(1030) }
{ model(3404) distribut(989) bayesian(671) }
{ extract(1171) text(1153) clinic(932) }
{ structur(1116) can(940) graph(676) }
{ error(1145) method(1030) estim(1020) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ sampl(1606) size(1419) use(1276) }
{ method(984) reconstruct(947) comput(926) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ use(976) code(926) identifi(902) }
{ network(2748) neural(1063) input(814) }
{ treatment(1704) effect(941) patient(846) }
{ howev(809) still(633) remain(590) }
{ studi(1119) effect(1106) posit(819) }
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{ survey(1388) particip(1329) question(1065) }
{ chang(1828) time(1643) increas(1301) }
{ case(1353) use(1143) diagnosi(1136) }
{ activ(1138) subject(705) human(624) }
{ estim(2440) model(1874) function(577) }
{ imag(2830) propos(1344) filter(1198) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ data(1714) softwar(1251) tool(1186) }
{ general(901) number(790) one(736) }
{ data(3963) clinic(1234) research(1004) }
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{ model(3480) simul(1196) paramet(876) }
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{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
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{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

CKGROUND: The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster.METHODS: We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data.RESULTS: When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging.CONCLUSIONS: We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm.

Resumo Limpo

ckground task spatial cluster detect involv find spatial region properti deviat norm expect valu probabilist set task can express find region event signific like usual spatial cluster detect interest field biosurveil mine astronom data militari surveil analysi fmri imag almost applic interest question whether cluster exist data exist interest find accur character clustermethod present general dynam program algorithm gridbas spatial cluster detect algorithm can use bayesian maximum aposteriori map estim like spatial distribut cluster bayesian model averag larg space spatial cluster distribut comput posterior probabl unusu spatial cluster algorithm explain evalu context biosurveil applic specif detect identif influenza outbreak base emerg depart visit relat simpl under model construct purpos evalu algorithm algorithm evalu use model semisynthet test dataresult compar baselin method test indic new algorithm can improv map estim certain condit greedi algorithm compar method found sensit smaller outbreak size outbreak increas term area affect proport individu affect method overtak greedi algorithm spatial precis recal new algorithm perform onpar baselin method task bayesian model averagingconclus conclud dynam program algorithm perform onpar avail method spatial cluster detect point low comput cost extend advantag favor research use algorithm

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