The data retrieval optimization from the perspective of evidence-based medicine
Vladimir Dobrynin, Julia Balykina, Michael Kamalov, Alexey Kolbin, Elena Verbitskaya, Munira Kasimova
Citation: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 5, pages 323–328 (2015)
Abstract. The paper is devoted to classification of MEDLINE abstracts into categories that correspond to types of medical interventions - types of patient treatments. This set of categories was extracted from Clinicaltrials.gov web site. Few classification algorithms were tested including Multinomial Naive Bayes, Multinomial Logistic Regression, and Linear SVM implementations from sklearn machine learning library. Document marking was based on the consideration of abstracts containing links to the Clinicaltrials.gov Web site. As the result of an automatical marking 3534 abstracts were marked for training and testing the set of algorithms metioned above. Best result of multinomial classification was achieved by Linear SVM with macro evaluation precision 70.06\%, recall 55.62\% and F-measure 62.01\%, and micro evaluation precision 64.91\%, recall 79.13\% and F-measure 71.32\%.