BMC Med Inform Decis Mak - Multi-topic assignment for exploratory navigation of consumer health information in NetWellness using formal concept analysis.

Tópicos

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

Resumo

CKGROUND: Finding quality consumer health information online can effectively bring important public health benefits to the general population. It can empower people with timely and current knowledge for managing their health and promoting wellbeing. Despite a popular belief that search engines such as Google can solve all information access problems, recent studies show that using search engines and simple search terms is not sufficient. Our objective is to provide an approach to organizing consumer health information for navigational exploration, complementing keyword-based direct search. Multi-topic assignment to health information, such as online questions, is a fundamental step for navigational exploration.METHODS: We introduce a new multi-topic assignment method combining semantic annotation using UMLS concepts (CUIs) and Formal Concept Analysis (FCA). Each question was tagged with CUIs identified by MetaMap. The CUIs were filtered with term-frequency and a new term-strength index to construct a CUI-question context. The CUI-question context and a topic-subject context were used for multi-topic assignment, resulting in a topic-question context. The topic-question context was then directly used for constructing a prototype navigational exploration interface.RESULTS: Experimental evaluation was performed on the task of automatic multi-topic assignment of 99 predefined topics for about 60,000 consumer health questions from NetWellness. Using example-based metrics, suitable for multi-topic assignment problems, our method achieved a precision of 0.849, recall of 0.774, and F1 measure of 0.782, using a reference standard of 278 questions with manually assigned topics. Compared to NetWellness' original topic assignment, a 36.5% increase in recall is achieved with virtually no sacrifice in precision.CONCLUSION: Enhancing the recall of multi-topic assignment without sacrificing precision is a prerequisite for achieving the benefits of navigational exploration. Our new multi-topic assignment method, combining term-strength, FCA, and information retrieval techniques, significantly improved recall and performed well according to example-based metrics.

Resumo Limpo

ckground find qualiti consum health inform onlin can effect bring import public health benefit general popul can empow peopl time current knowledg manag health promot wellb despit popular belief search engin googl can solv inform access problem recent studi show use search engin simpl search term suffici object provid approach organ consum health inform navig explor complement keywordbas direct search multitop assign health inform onlin question fundament step navig explorationmethod introduc new multitop assign method combin semant annot use uml concept cui formal concept analysi fca question tag cui identifi metamap cui filter termfrequ new termstrength index construct cuiquest context cuiquest context topicsubject context use multitop assign result topicquest context topicquest context direct use construct prototyp navig explor interfaceresult experiment evalu perform task automat multitop assign predefin topic consum health question netwel use examplebas metric suitabl multitop assign problem method achiev precis recal f measur use refer standard question manual assign topic compar netwel origin topic assign increas recal achiev virtual sacrific precisionconclus enhanc recal multitop assign without sacrif precis prerequisit achiev benefit navig explor new multitop assign method combin termstrength fca inform retriev techniqu signific improv recal perform well accord examplebas metric

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