AMIA Annu Symp Proc - Pharmacovigilance on twitter? Mining tweets for adverse drug reactions.

Tópicos

{ extract(1171) text(1153) clinic(932) }
{ drug(1928) target(777) effect(648) }
{ health(1844) social(1437) communiti(874) }
{ measur(2081) correl(1212) valu(896) }
{ error(1145) method(1030) estim(1020) }
{ data(1714) softwar(1251) tool(1186) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ group(2977) signific(1463) compar(1072) }
{ result(1111) use(1088) new(759) }
{ decis(3086) make(1611) patient(1517) }
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{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ 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) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
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{ activ(1452) weight(1219) physic(1104) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Recent research has shown that Twitter data analytics can have broad implications on public health research. However, its value for pharmacovigilance has been scantly studied - with health related forums and community support groups preferred for the task. We present a systematic study of tweets collected for 74 drugs to assess their value as sources of potential signals for adverse drug reactions (ADRs). We created an annotated corpus of 10,822 tweets. Each tweet was annotated for the presence or absence of ADR mentions, with the span and Unified Medical Language System (UMLS) concept ID noted for each ADR present. Using Cohen's kappa1, we calculated the inter-annotator agreement (IAA) for the binary annotations to be 0.69. To demonstrate the utility of the corpus, we attempted a lexicon-based approach for concept extraction, with promising success (54.1% precision, 62.1% recall, and 57.8% F-measure). A subset of the corpus is freely available at: http://diego.asu.edu/downloads.

Resumo Limpo

recent research shown twitter data analyt can broad implic public health research howev valu pharmacovigil scant studi health relat forum communiti support group prefer task present systemat studi tweet collect drug assess valu sourc potenti signal advers drug reaction adr creat annot corpus tweet tweet annot presenc absenc adr mention span unifi medic languag system uml concept id note adr present use cohen kappa calcul interannot agreement iaa binari annot demonstr util corpus attempt lexiconbas approach concept extract promis success precis recal fmeasur subset corpus freeli avail httpdiegoasuedudownload

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