Inform Health Soc Care - A model based on multi-features to enhance healthcare and medical document retrieval.

Tópicos

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

Resumo

JECTIVE: A major problem in biomedical informatics is the contextual retrieval and ranking of medical and healthcare information. In this article, we present a model for extracting semantic relations among medical and clinical documents. The purpose is to maximise contextual retrieval and ranking performance with minimum input from users.DESIGN: We developed and evaluated a medical search engine that relies on a multi-features similarity model. The indexed documents are represented as a network that reflects the semantic relations among documents to assess topical rankings.MEASUREMENT: The evaluation measurements include the following: recall, precision and R-precision. We used OHSUMED collection to evaluate our work with runs submitted to TREC-9. We provide a comparison of the top five runs that achieved the highest average precision scores. In addition, we used questionnaire-based evaluation to measure the effectiveness of the ranking task.RESULTS: The results indicated that the proposed model achieved a higher average precision in comparison with top-scored runs submitted to TREC-9; the improvement of our model over other methods is statistically significant (p-value<0.0001). Furthermore, a questionnaire-based experiment showed that the proposed model performed quite well in ranking retrieved documents according to their topics.

Resumo Limpo

jectiv major problem biomed informat contextu retriev rank medic healthcar inform articl present model extract semant relat among medic clinic document purpos maximis contextu retriev rank perform minimum input usersdesign develop evalu medic search engin reli multifeatur similar model index document repres network reflect semant relat among document assess topic rankingsmeasur evalu measur includ follow recal precis rprecis use ohsum collect evalu work run submit trec provid comparison top five run achiev highest averag precis score addit use questionnairebas evalu measur effect rank taskresult result indic propos model achiev higher averag precis comparison topscor run submit trec improv model method statist signific pvalu furthermor questionnairebas experi show propos model perform quit well rank retriev document accord topic

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