Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
The data retrieval optimization from the perspective of evidence-based medicine. / Dobrynin, Vladimir; Balykina, Julia; Kamalov, Michael; Kolbin, Alexey; Verbitskaya, Elena; Kasimova, Munira.
PROCEEDINGS OF THE 2015 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS. ed. / M Ganzha; L Maciaszek; M Paprzycki. Institute of Electrical and Electronics Engineers Inc., 2015. p. 323-328 (ACSIS-Annals of Computer Science and Information Systems; Vol. 5).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
}
TY - GEN
T1 - The data retrieval optimization from the perspective of evidence-based medicine
AU - Dobrynin, Vladimir
AU - Balykina, Julia
AU - Kamalov, Michael
AU - Kolbin, Alexey
AU - Verbitskaya, Elena
AU - Kasimova, Munira
PY - 2015
Y1 - 2015
N2 - 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%.
AB - 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%.
U2 - 10.15439/2015F130
DO - 10.15439/2015F130
M3 - статья в сборнике материалов конференции
SN - 9788360810651
T3 - ACSIS-Annals of Computer Science and Information Systems
SP - 323
EP - 328
BT - PROCEEDINGS OF THE 2015 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS
A2 - Ganzha, M
A2 - Maciaszek, L
A2 - Paprzycki, M
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 September 2015 through 15 September 2015
ER -
ID: 3983165