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 in...
| Published in: | BioMed Research International Vol. 2016; pp. 1 - 11 |
|---|---|
| Main Authors: | , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
9/7/2016
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117919279&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117919279 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/7/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 117919279 117919279 117919279 10.1155/2016/8313454 117919279 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Natural Language Processing Based Instrument for Classification of Free Text Medical Records. aug: au: Khachidze, Manana Tsintsadze, Magda Archuadze, Maia affil: Ivane Javakhishvili Tbilisi State University, University St. 3, 0179 Tbilisi, Georgia sug: subj: Medical Records Classification Language Human Georgia Geographic Locations ab: 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) and K-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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|