AMIA Annu Symp Proc - Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ featur(3375) classif(2383) classifi(1994) }
{ learn(2355) train(1041) set(1003) }
{ method(1557) propos(1049) approach(1037) }
{ method(2212) result(1239) propos(1039) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ perform(999) metric(946) measur(919) }
{ structur(1116) can(940) graph(676) }
{ data(1737) use(1416) pattern(1282) }
{ general(901) number(790) one(736) }
{ model(2656) set(1616) predict(1553) }
{ cancer(2502) breast(956) screen(824) }
{ can(774) often(719) complex(702) }
{ measur(2081) correl(1212) valu(896) }
{ bind(1733) structur(1185) ligand(1036) }
{ care(1570) inform(1187) nurs(1089) }
{ featur(1941) imag(1645) propos(1176) }
{ system(1050) medic(1026) inform(1018) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ data(3008) multipl(1320) sourc(1022) }
{ can(981) present(881) function(850) }
{ use(976) code(926) identifi(902) }
{ method(1969) cluster(1462) data(1082) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ sequenc(1873) structur(1644) protein(1328) }
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{ network(2748) neural(1063) input(814) }
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{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
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{ assess(1506) score(1403) qualiti(1306) }
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{ framework(1458) process(801) describ(734) }
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{ error(1145) method(1030) estim(1020) }
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{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
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{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(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) }
{ studi(1119) effect(1106) posit(819) }
{ 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) }
{ research(1218) medic(880) student(794) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ 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) }
{ 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) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ 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

Automated Word Sense Disambiguation in clinical documents is a prerequisite to accurate extraction of medical information. Emerging methods utilizing hyperdimensional computing present new approaches to this problem. In this paper, we evaluate one such approach, the Binary Spatter Code Word Sense Disambiguation algorithm, on 50 ambiguous abbreviation sets derived from clinical notes. This algorithm uses reversible vector transformations to encode ambiguous terms and their context-specific senses into vectors representing surrounding terms. The sense for a new context is then inferred from vectors representing the terms it contains. One-to-one BSC-WSD achieves average accuracy of 94.55% when considering the orientation and distance of neighboring terms relative to the target abbreviation, outperforming Support Vector Machine and Na?ve Bayes classifiers. Furthermore, it is practical to deal with all 50 abbreviations in an identical manner using a single one-to-many BSC-WSD model with average accuracy of 93.91%, which is not possible with common machine learning algorithms.

Resumo Limpo

autom word sens disambigu clinic document prerequisit accur extract medic inform emerg method util hyperdimension comput present new approach problem paper evalu one approach binari spatter code word sens disambigu algorithm ambigu abbrevi set deriv clinic note algorithm use revers vector transform encod ambigu term contextspecif sens vector repres surround term sens new context infer vector repres term contain onetoon bscwsd achiev averag accuraci consid orient distanc neighbor term relat target abbrevi outperform support vector machin nave bay classifi furthermor practic deal abbrevi ident manner use singl onetomani bscwsd model averag accuraci possibl common machin learn algorithm

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