Natural Language Processing Based Instrument for Classification of Free Text Medical Records.
According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) andK-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a "shrink" method, based on features selection, was introduced and applied. At the first stage of classification the results of the "shrink" case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful.
Ivane Javakhishvili Tbilisi State University, University St. 3, 0179 Tbilisi, Georgia.
Manana
0000-0003-4545-2679
M
Khachidze
Ivane Javakhishvili Tbilisi State University, University St. 3, 0179 Tbilisi, Georgia.
Magda
0000-0001-9316-3295
M
Tsintsadze
Ivane Javakhishvili Tbilisi State University, University St. 3, 0179 Tbilisi, Georgia.
Maia
0000-0002-9484-1016
M
Archuadze
Proc AMIA Symp. 2001;:17-21
11825149
J Am Med Inform Assoc. 2014 Sep-Oct;21(5):871-5
24786209
Bioinformatics. 2007 Oct 1;23(19):2507-17
17720704
J Am Med Inform Assoc. 2011 Sep-Oct;18(5):544-51
21846786
Methods Inf Med. 1993 Aug;32(4):281-91
8412823
J Biomed Inform. 2015 Feb;53:196-207
25451103
J Biomed Inform. 2009 Oct;42(5):760-72
19683066
J Biomed Inform. 2015 Apr;54:77-84
25661260
J Biomed Inform. 2015 Dec;58 Suppl:S92-S102
26241355
J Med Syst. 2012 Dec;36(6):3861-74
22592391
BMC Bioinformatics. 2008 Apr 11;9 Suppl 3:S10
18426545