Geospat Health - Redefining climate regions in the United States of America using satellite remote sensing and machine learning for public health applications.

Tópicos

{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ can(774) often(719) complex(702) }
{ measur(2081) correl(1212) valu(896) }
{ method(1557) propos(1049) approach(1037) }
{ risk(3053) factor(974) diseas(938) }
{ state(1844) use(1261) util(961) }
{ data(2317) use(1299) case(1017) }
{ use(2086) technolog(871) perceiv(783) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ time(1939) patient(1703) rate(768) }
{ high(1669) rate(1365) level(1280) }
{ detect(2391) sensit(1101) algorithm(908) }
{ imag(1947) propos(1133) code(1026) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ care(1570) inform(1187) nurs(1089) }
{ drug(1928) target(777) effect(648) }
{ process(1125) use(805) approach(778) }
{ model(3404) distribut(989) bayesian(671) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ imag(2675) segment(2577) method(1081) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ chang(1828) time(1643) increas(1301) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ perform(999) metric(946) measur(919) }
{ studi(1119) effect(1106) posit(819) }
{ age(1611) year(1155) adult(843) }
{ first(2504) two(1366) second(1323) }
{ activ(1138) subject(705) human(624) }
{ decis(3086) make(1611) patient(1517) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ featur(1941) imag(1645) propos(1176) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Existing climate classification has not been designed for an efficient handling of public health scenarios. This work aims to design an objective spatial climate regionalization method for assessing health risks in response to extreme weather. Specific climate regions for the conterminous United States of America (USA) were defined using satellite remote sensing (RS) data and compared with the conventional K?ppen-Geiger (KG) divisions. Using the nationwide database of hospitalisations among the elderly (=65 year olds), we examined the utility of a RS-based climate regionalization to assess public health risk due to extreme weather, by comparing the rate of hospitalisations in response to thermal extremes across climatic regions. Satellite image composites from 2002-2012 were aggregated, masked and compiled into a multi-dimensional dataset. The conterminous USA was classified into 8 distinct regions using a stepwise regionalization approach to limit noise and collinearity (LKN), which exhibited a high degree of consistency with the KG regions and a well-defined regional delineation by annual and seasonal temperature and precipitation values. The most populous was a temperate wet region (10.9 million), while the highest rate of hospitalisations due to exposure to heat and cold (9.6 and 17.7 cases per 100,000 persons at risk, respectively) was observed in the relatively warm and humid south-eastern region. RS-based regionalization demonstrates strong potential for assessing the adverse effects of severe weather on human health and for decision support. Its utility in forecasting and mitigating these effects has to be further explored.

Resumo Limpo

exist climat classif design effici handl public health scenario work aim design object spatial climat region method assess health risk respons extrem weather specif climat region contermin unit state america usa defin use satellit remot sens rs data compar convent kppengeig kg divis use nationwid databas hospitalis among elder year old examin util rsbase climat region assess public health risk due extrem weather compar rate hospitalis respons thermal extrem across climat region satellit imag composit aggreg mask compil multidimension dataset contermin usa classifi distinct region use stepwis region approach limit nois collinear lkn exhibit high degre consist kg region welldefin region delin annual season temperatur precipit valu popul temper wet region million highest rate hospitalis due exposur heat cold case per person risk respect observ relat warm humid southeastern region rsbase region demonstr strong potenti assess advers effect sever weather human health decis support util forecast mitig effect explor

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