Methods Inf Med - Multidimensional point transform for public health practice.

Tópicos

{ framework(1458) process(801) describ(734) }
{ system(1050) medic(1026) inform(1018) }
{ method(1969) cluster(1462) data(1082) }
{ health(3367) inform(1360) care(1135) }
{ implement(1333) system(1263) develop(1122) }
{ perform(999) metric(946) measur(919) }
{ data(2317) use(1299) case(1017) }
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{ spatial(1525) area(1432) region(1030) }
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{ imag(2830) propos(1344) filter(1198) }
{ method(984) reconstruct(947) comput(926) }
{ model(2656) set(1616) predict(1553) }
{ use(2086) technolog(871) perceiv(783) }
{ result(1111) use(1088) new(759) }
{ detect(2391) sensit(1101) algorithm(908) }
{ chang(1828) time(1643) increas(1301) }
{ clinic(1479) use(1117) guidelin(835) }
{ care(1570) inform(1187) nurs(1089) }
{ compound(1573) activ(1297) structur(1058) }
{ time(1939) patient(1703) rate(768) }
{ analysi(2126) use(1163) compon(1037) }
{ data(1737) use(1416) pattern(1282) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
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{ studi(1410) differ(1259) use(1210) }
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{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

CKGROUND: With increases in spatial information and enabling technologies, location-privacy concerns have been on the rise. A commonly proposed solution in public health involves random perturbation, however consideration for individual dimensions (attributes) has been weak.OBJECTIVES: The current study proposes a multidimensional point transform (MPT) that integrates the spatial dimension with other dimensions of interest to comprehensively anonymise data.METHODS: The MPT relies on the availability of a base population, a subset patient dataset, and shared dimensions of interest. Perturbation distance and anonymity thresholds are defined, as are allowable dimensional perturbations. A preliminary implementation is presented using sex, age and location as the three dimensions of interest, with a maximum perturbation distance of 1 kilometre and an anonymity threshold of 20%. A synthesised New York county population is used for testing with 1000 iterations for each of 25, 50, 100, 200 and 400 patient dataset sizes.RESULTS: The MPT consistently yielded a mean perturbation distance of 46 metres with no sex or age perturbation required. Displacement of the spatial mean decreased with patient dataset size and averaged 5.6 metres overall.CONCLUSIONS: The MPT presents a flexible, customisable and adaptive algorithm for perturbing datasets for public health, allowing tweaking and optimisation of the trade-offs for different datasets and purposes. It is not, however, a substitute for secure and ethical conduct, and a public health framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information is required. The MPT presents an important component of such a framework.

Resumo Limpo

ckground increas spatial inform enabl technolog locationprivaci concern rise common propos solut public health involv random perturb howev consider individu dimens attribut weakobject current studi propos multidimension point transform mpt integr spatial dimens dimens interest comprehens anonymis datamethod mpt reli avail base popul subset patient dataset share dimens interest perturb distanc anonym threshold defin allow dimension perturb preliminari implement present use sex age locat three dimens interest maximum perturb distanc kilometr anonym threshold synthesis new york counti popul use test iter patient dataset sizesresult mpt consist yield mean perturb distanc metr sex age perturb requir displac spatial mean decreas patient dataset size averag metr overallconclus mpt present flexibl customis adapt algorithm perturb dataset public health allow tweak optimis tradeoff differ dataset purpos howev substitut secur ethic conduct public health framework appropri disclosur use dissemin data contain person identifi inform requir mpt present import compon framework

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