BMC Med Inform Decis Mak - Automated systems to identify relevant documents in product risk management.

Tópicos

{ detect(2391) sensit(1101) algorithm(908) }
{ studi(2440) review(1878) systemat(933) }
{ general(901) number(790) one(736) }
{ implement(1333) system(1263) develop(1122) }
{ sampl(1606) size(1419) use(1276) }
{ model(2341) predict(2261) use(1141) }
{ risk(3053) factor(974) diseas(938) }
{ model(2656) set(1616) predict(1553) }
{ drug(1928) target(777) effect(648) }
{ can(774) often(719) complex(702) }
{ health(3367) inform(1360) care(1135) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ extract(1171) text(1153) clinic(932) }
{ search(2224) databas(1162) retriev(909) }
{ system(1050) medic(1026) inform(1018) }
{ imag(1057) registr(996) error(939) }
{ error(1145) method(1030) estim(1020) }
{ concept(1167) ontolog(924) domain(897) }
{ studi(1410) differ(1259) use(1210) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ high(1669) rate(1365) level(1280) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
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{ data(1714) softwar(1251) tool(1186) }
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{ control(1307) perform(991) simul(935) }
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{ blood(1257) pressur(1144) flow(957) }
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{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
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{ state(1844) use(1261) util(961) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
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{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

CKGROUND: Product risk management involves critical assessment of the risks and benefits of health products circulating in the market. One of the important sources of safety information is the primary literature, especially for newer products which regulatory authorities have relatively little experience with. Although the primary literature provides vast and diverse information, only a small proportion of which is useful for product risk assessment work. Hence, the aim of this study is to explore the possibility of using text mining to automate the identification of useful articles, which will reduce the time taken for literature search and hence improving work efficiency. In this study, term-frequency inverse document-frequency values were computed for predictors extracted from the titles and abstracts of articles related to three tumour necrosis factors-alpha blockers. A general automated system was developed using only general predictors and was tested for its generalizability using articles related to four other drug classes. Several specific automated systems were developed using both general and specific predictors and training sets of different sizes in order to determine the minimum number of articles required for developing such systems.RESULTS: The general automated system had an area under the curve value of 0.731 and was able to rank 34.6% and 46.2% of the total number of 'useful' articles among the first 10% and 20% of the articles presented to the evaluators when tested on the generalizability set. However, its use may be limited by the subjective definition of useful articles. For the specific automated system, it was found that only 20 articles were required to develop a specific automated system with a prediction performance (AUC 0.748) that was better than that of general automated system.CONCLUSIONS: Specific automated systems can be developed rapidly and avoid problems caused by subjective definition of useful articles. Thus the efficiency of product risk management can be improved with the use of specific automated systems.

Resumo Limpo

ckground product risk manag involv critic assess risk benefit health product circul market one import sourc safeti inform primari literatur especi newer product regulatori author relat littl experi although primari literatur provid vast divers inform small proport use product risk assess work henc aim studi explor possibl use text mine autom identif use articl will reduc time taken literatur search henc improv work effici studi termfrequ invers documentfrequ valu comput predictor extract titl abstract articl relat three tumour necrosi factorsalpha blocker general autom system develop use general predictor test generaliz use articl relat four drug class sever specif autom system develop use general specif predictor train set differ size order determin minimum number articl requir develop systemsresult general autom system area curv valu abl rank total number use articl among first articl present evalu test generaliz set howev use may limit subject definit use articl specif autom system found articl requir develop specif autom system predict perform auc better general autom systemconclus specif autom system can develop rapid avoid problem caus subject definit use articl thus effici product risk manag can improv use specif autom system

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