AMIA Annu Symp Proc - Extracting patient demographics and personal medical information from online health forums.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ health(1844) social(1437) communiti(874) }
{ health(3367) inform(1360) care(1135) }
{ implement(1333) system(1263) develop(1122) }
{ patient(2837) hospit(1953) medic(668) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ can(774) often(719) complex(702) }
{ howev(809) still(633) remain(590) }
{ can(981) present(881) function(850) }
{ use(976) code(926) identifi(902) }
{ inform(2794) health(2639) internet(1427) }
{ framework(1458) process(801) describ(734) }
{ method(1557) propos(1049) approach(1037) }
{ research(1085) discuss(1038) issu(1018) }
{ perform(1367) use(1326) method(1137) }
{ 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) }
{ studi(2440) review(1878) systemat(933) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ design(1359) user(1324) use(1319) }
{ case(1353) use(1143) diagnosi(1136) }
{ perform(999) metric(946) measur(919) }
{ compound(1573) activ(1297) structur(1058) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ data(3008) multipl(1320) sourc(1022) }
{ activ(1138) subject(705) human(624) }
{ structur(1116) can(940) graph(676) }
{ activ(1452) weight(1219) physic(1104) }
{ model(3404) distribut(989) bayesian(671) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ problem(2511) optim(1539) algorithm(950) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ data(1714) softwar(1251) tool(1186) }
{ 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) }
{ search(2224) databas(1162) retriev(909) }
{ featur(1941) imag(1645) propos(1176) }
{ 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) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ 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) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ survey(1388) particip(1329) question(1065) }
{ 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) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Natural language processing has been successfully leveraged to extract patient information from unstructured clinical text. However the majority of the existing work targets at obtaining a specific category of clinical information through individual efforts. In the midst of the Health 2.0 wave, online health forums increasingly host abundant and diverse health-related information regarding the demographics and medical information of patients who are either actively participating in or passively reported at these forums. The potential categories of such information span a wide spectrum, whose extraction requires a systematic and comprehensive approach beyond the traditional isolated efforts that specialize in harvesting information of single categories. In this paper, we develop a new integrated biomedical NLP pipeline that automatically extracts a comprehensive set of patient demographics and medical information from online health forums. The pipeline can be adopted to construct structured personal health profiles from unstructured user-contributed content on eHealth social media sites. This paper describes key aspects of the pipeline as well as reports experimental results that show the system's satisfactory performance in accomplishing a series of NLP tasks of extracting patient information from online health forums.

Resumo Limpo

natur languag process success leverag extract patient inform unstructur clinic text howev major exist work target obtain specif categori clinic inform individu effort midst health wave onlin health forum increas host abund divers healthrel inform regard demograph medic inform patient either activ particip passiv report forum potenti categori inform span wide spectrum whose extract requir systemat comprehens approach beyond tradit isol effort special harvest inform singl categori paper develop new integr biomed nlp pipelin automat extract comprehens set patient demograph medic inform onlin health forum pipelin can adopt construct structur person health profil unstructur usercontribut content ehealth social media site paper describ key aspect pipelin well report experiment result show system satisfactori perform accomplish seri nlp task extract patient inform onlin health forum

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