J Biomed Inform - Dynamic categorization of clinical research eligibility criteria by hierarchical clustering.

Tópicos

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{ featur(3375) classif(2383) classifi(1994) }
{ studi(2440) review(1878) systemat(933) }
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{ model(2656) set(1616) predict(1553) }
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{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ 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) }
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{ survey(1388) particip(1329) question(1065) }
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{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }

Resumo

JECTIVE: To semi-automatically induce semantic categories of eligibility criteria from text and to automatically classify eligibility criteria based on their semantic similarity.DESIGN: The UMLS semantic types and a set of previously developed semantic preference rules were utilized to create an unambiguous semantic feature representation to induce eligibility criteria categories through hierarchical clustering and to train supervised classifiers.MEASUREMENTS: We induced 27 categories and measured the prevalence of the categories in 27,278 eligibility criteria from 1578 clinical trials and compared the classification performance (i.e., precision, recall, and F1-score) between the UMLS-based feature representation and the "bag of words" feature representation among five common classifiers in Weka, including J48, Bayesian Network, Na?ve Bayesian, Nearest Neighbor, and instance-based learning classifier.RESULTS: The UMLS semantic feature representation outperforms the "bag of words" feature representation in 89% of the criteria categories. Using the semantically induced categories, machine-learning classifiers required only 2000 instances to stabilize classification performance. The J48 classifier yielded the best F1-score and the Bayesian Network classifier achieved the best learning efficiency.CONCLUSION: The UMLS is an effective knowledge source and can enable an efficient feature representation for semi-automated semantic category induction and automatic categorization for clinical research eligibility criteria and possibly other clinical text.

Resumo Limpo

jectiv semiautomat induc semant categori elig criteria text automat classifi elig criteria base semant similaritydesign uml semant type set previous develop semant prefer rule util creat unambigu semant featur represent induc elig criteria categori hierarch cluster train supervis classifiersmeasur induc categori measur preval categori elig criteria clinic trial compar classif perform ie precis recal fscore umlsbas featur represent bag word featur represent among five common classifi weka includ j bayesian network nave bayesian nearest neighbor instancebas learn classifierresult uml semant featur represent outperform bag word featur represent criteria categori use semant induc categori machinelearn classifi requir instanc stabil classif perform j classifi yield best fscore bayesian network classifi achiev best learn efficiencyconclus uml effect knowledg sourc can enabl effici featur represent semiautom semant categori induct automat categor clinic research elig criteria possibl clinic text

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