IEEE J Biomed Health Inform - Network-based modeling and intelligent data mining of social media for improving care.

Tópicos

{ health(1844) social(1437) communiti(874) }
{ data(1737) use(1416) pattern(1282) }
{ treatment(1704) effect(941) patient(846) }
{ decis(3086) make(1611) patient(1517) }
{ data(3963) clinic(1234) research(1004) }
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{ imag(1947) propos(1133) code(1026) }
{ measur(2081) correl(1212) valu(896) }
{ featur(3375) classif(2383) classifi(1994) }
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{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
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{ visual(1396) interact(850) tool(830) }
{ blood(1257) pressur(1144) flow(957) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
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{ sequenc(1873) structur(1644) protein(1328) }
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{ compound(1573) activ(1297) structur(1058) }
{ record(1888) medic(1808) patient(1693) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
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{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ 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) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users' forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from users' forum posts. We then introduced a novel network-based approach for modeling users' forum interactions and employed a network partitioning method based on optimizing a stability quality measure. This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived from both word-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness (or ineffectiveness) of future treatments.

Resumo Limpo

intellig extract knowledg social media recent attract great interest biomed health informat communiti simultan improv healthcar outcom reduc cost use consumergener opinion propos twostep analysi framework focus posit negat sentiment well side effect treatment user forum post identifi user communiti modul influenti user purpos ascertain user opinion cancer treatment use selforgan map analyz word frequenc data deriv user forum post introduc novel networkbas approach model user forum interact employ network partit method base optim stabil qualiti measur allow us determin consum opinion identifi influenti user within retriev modul use inform deriv wordfrequ data networkbas properti approach can expand research intellig mine social media data consum opinion various treatment provid rapid uptod inform pharmaceut industri hospit medic staff effect ineffect futur treatment

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