AMIA Annu Symp Proc - SNOMED CT Saves Keystrokes: Quantifying Semantic Autocompletion.

Tópicos

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{ perform(999) metric(946) measur(919) }
{ structur(1116) can(940) graph(676) }
{ problem(2511) optim(1539) algorithm(950) }
{ extract(1171) text(1153) clinic(932) }
{ design(1359) user(1324) use(1319) }
{ result(1111) use(1088) new(759) }
{ inform(2794) health(2639) internet(1427) }
{ can(774) often(719) complex(702) }
{ measur(2081) correl(1212) valu(896) }
{ patient(2315) diseas(1263) diabet(1191) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ concept(1167) ontolog(924) domain(897) }
{ method(984) reconstruct(947) comput(926) }
{ data(3963) clinic(1234) research(1004) }
{ research(1085) discuss(1038) issu(1018) }
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{ ehr(2073) health(1662) electron(1139) }
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{ care(1570) inform(1187) nurs(1089) }
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{ studi(1410) differ(1259) use(1210) }
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{ system(1050) medic(1026) inform(1018) }
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{ perform(1367) use(1326) method(1137) }
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{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ 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) }
{ signal(2180) analysi(812) frequenc(800) }
{ 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) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ 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) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In many applications autocompletion functionality saves keystrokes, increases user experience, and helps the user to comply with standardized terminology. Intuitively, the more context information we have about the user, the more accurate autocompletion suggestions we can give. In this paper we research the added value of contextual information for autocompletion algorithms, measured as the average number of saved keystrokes. In our experiments, a context is represented as a set of SNOMED CT terms. Using the structure of SNOMED CT we determine the semantic distance of each SNOMED CT term to the context terms. The resulting distance function is injected in the autocompletion algorithms to reward terms that are semantically close to the context. Our results show that semantic enhancement saves up to 18% of keystrokes, in addition to the percentage of keystrokes saved for the non-semantic base algorithm.

Resumo Limpo

mani applic autocomplet function save keystrok increas user experi help user compli standard terminolog intuit context inform user accur autocomplet suggest can give paper research ad valu contextu inform autocomplet algorithm measur averag number save keystrok experi context repres set snome ct term use structur snome ct determin semant distanc snome ct term context term result distanc function inject autocomplet algorithm reward term semant close context result show semant enhanc save keystrok addit percentag keystrok save nonsemant base algorithm

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