J Biomed Inform - Assertion modeling and its role in clinical phenotype identification.

Tópicos

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{ state(1844) use(1261) util(961) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ 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) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ cancer(2502) breast(956) screen(824) }
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{ survey(1388) particip(1329) question(1065) }
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{ 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

This paper describes an approach to assertion classification and an empirical study on the impact this task has on phenotype identification, a real world application in the clinical domain. The task of assertion classification is to assign to each medical concept mentioned in a clinical report (e.g., pneumonia, chest pain) a specific assertion category (e.g., present, absent, and possible). To improve the classification of medical assertions, we propose several new features that capture the semantic properties of special cue words highly indicative of a specific assertion category. The results obtained outperform the current state-of-the-art results for this task. Furthermore, we confirm the intuition that assertion classification contributes in significantly improving the results of phenotype identification from free-text clinical records.

Resumo Limpo

paper describ approach assert classif empir studi impact task phenotyp identif real world applic clinic domain task assert classif assign medic concept mention clinic report eg pneumonia chest pain specif assert categori eg present absent possibl improv classif medic assert propos sever new featur captur semant properti special cue word high indic specif assert categori result obtain outperform current stateoftheart result task furthermor confirm intuit assert classif contribut signific improv result phenotyp identif freetext clinic record

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