Automated Classification of Clinical Incident Types...Australian National Health Informatics Conference 2015
We consider the task of automatic classification of clinical incident reports using machine learning methods. Our data consists of 5448 clinical incident reports collected from the Incident Information Management System used by 7 hospitals in the state of New South Wales in Australia. We evaluate th...
| Publicado en: | Studies in Health Technology & Informatics Vol. 214; pp. 87 - 94 |
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| Autores principales: | , , |
| Formato: | equations & formulas proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jul2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127746207&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127746207 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: Jul2015 vid: 214 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 127746207 127746207 127746207 10.3233/978-1-61499-558-6-87 127746207 ppf: 87 ppct: 7 formats: tig: atl: Automated Classification of Clinical Incident Types...Australian National Health Informatics Conference 2015 aug: au: GUPTA, Jaiprakash KOPRINSKA, Irena PATRICK, Jon affil: School of Information Technologies, University of Sydney, Australia sug: subj: Incident Reports Classification Algorithms Machine Learning Human Congresses and Conferences Australia Australia Experimental Studies ROC Curve kappa Statistic ab: We consider the task of automatic classification of clinical incident reports using machine learning methods. Our data consists of 5448 clinical incident reports collected from the Incident Information Management System used by 7 hospitals in the state of New South Wales in Australia. We evaluate the performance of four classification algorithms: decision tree, naïve Bayes, multinomial naïve Bayes and support vector machine. We initially consider 13 classes (incident types) that were then reduced to 12, and show that it is possible to build accurate classifiers. The most accurate classifier was the multinomial naïve Bayes achieving accuracy of 80.44% and AUC of 0.91. We also investigate the effect of class labelling by an ordinary clinician and an expert, and show that when the data is labelled by an expert the classification performance of all classifiers improves. We found that again the best classifier was multinomial naïve Bayes achieving accuracy of 81.32% and AUC of 0.97. Our results show that some classes in the Incident Information Management System such as Primary Care are not distinct and their removal can improve performance; some other classes such as Aggression Victim are easier to classify than others such as Behavior and Human Performance. In summary, we show that the classification performance can be improved by expert class labelling of the training data, removing classes that are not well defined and selecting appropriate machine learning classifiers. pubtype: Academic Journal doctype: equations & formulas proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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