Artif Intell Med - Exploiting the systematic review protocol for classification of medical abstracts.

Tópicos

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Resumo

JECTIVE: To determine whether the automatic classification of documents can be useful in systematic reviews on medical topics, and specifically if the performance of the automatic classification can be enhanced by using the particular protocol of questions employed by the human reviewers to create multiple classifiers.METHODS AND MATERIALS: The test collection is the data used in large-scale systematic review on the topic of the dissemination strategy of health care services for elderly people. From a group of 47,274 abstracts marked by human reviewers to be included in or excluded from further screening, we randomly selected 20,000 as a training set, with the remaining 27,274 becoming a separate test set. As a machine learning algorithm we used complement na?ve Bayes. We tested both a global classification method, where a single classifier is trained on instances of abstracts and their classification (i.e., included or excluded), and a novel per-question classification method that trains multiple classifiers for each abstract, exploiting the specific protocol (questions) of the systematic review. For the per-question method we tested four ways of combining the results of the classifiers trained for the individual questions. As evaluation measures, we calculated precision and recall for several settings of the two methods. It is most important not to exclude any relevant documents (i.e., to attain high recall for the class of interest) but also desirable to exclude most of the non-relevant documents (i.e., to attain high precision on the class of interest) in order to reduce human workload.RESULTS: For the global method, the highest recall was 67.8% and the highest precision was 37.9%. For the per-question method, the highest recall was 99.2%, and the highest precision was 63%. The human-machine workflow proposed in this paper achieved a recall value of 99.6%, and a precision value of 17.8%.CONCLUSION: The per-question method that combines classifiers following the specific protocol of the review leads to better results than the global method in terms of recall. Because neither method is efficient enough to classify abstracts reliably by itself, the technology should be applied in a semi-automatic way, with a human expert still involved. When the workflow includes one human expert and the trained automatic classifier, recall improves to an acceptable level, showing that automatic classification techniques can reduce the human workload in the process of building a systematic review.

Resumo Limpo

jectiv determin whether automat classif document can use systemat review medic topic specif perform automat classif can enhanc use particular protocol question employ human review creat multipl classifiersmethod materi test collect data use largescal systemat review topic dissemin strategi health care servic elder peopl group abstract mark human review includ exclud screen random select train set remain becom separ test set machin learn algorithm use complement nave bay test global classif method singl classifi train instanc abstract classif ie includ exclud novel perquest classif method train multipl classifi abstract exploit specif protocol question systemat review perquest method test four way combin result classifi train individu question evalu measur calcul precis recal sever set two method import exclud relev document ie attain high recal class interest also desir exclud nonrelev document ie attain high precis class interest order reduc human workloadresult global method highest recal highest precis perquest method highest recal highest precis humanmachin workflow propos paper achiev recal valu precis valu conclus perquest method combin classifi follow specif protocol review lead better result global method term recal neither method effici enough classifi abstract reliabl technolog appli semiautomat way human expert still involv workflow includ one human expert train automat classifi recal improv accept level show automat classif techniqu can reduc human workload process build systemat review

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