Methods Inf Med - Chi-square-based scoring function for categorization of MEDLINE citations.

Tópicos

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{ patient(2837) hospit(1953) medic(668) }
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{ can(981) present(881) function(850) }
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{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
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{ result(1111) use(1088) new(759) }
{ 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(2212) result(1239) propos(1039) }

Resumo

JECTIVES: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic.METHODS: Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain.RESULTS: Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method by comparing it to three machine-learning algorithms (support vector machines, decision trees, na?ve Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine-learning algorithms.CONCLUSIONS: We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA literature-based discovery support system as a preprocessor for gene symbol disambiguation process.

Resumo Limpo

jectiv text categor use biomed informat identifi document contain relev topic interest develop simpl method use chisquarebas score function determin likelihood medlin citat contain genet relev topicmethod procedur requir construct genet nongenet domain document corpus use mesh descriptor assign medlin citat categor task compar frequenc mesh descriptor two corpora appli chisquar test mesh descriptor consid posit indic relat observ frequenc genet domain corpus greater relat observ frequenc nongenet domain corpus output propos method list score citat highest score given citat contain mesh descriptor typic genet domainresult valid done set manual annot medlin citat achiev predict accuraci recal precis evalu method compar three machinelearn algorithm support vector machin decis tree nave bay although differ statist signific differ result show chisquar score perform good compar machinelearn algorithmsconclus suggest chisquar score effect solut help categor medlin citat algorithm implement bitola literaturebas discoveri support system preprocessor gene symbol disambigu process

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