Clinical Safety Incident Taxonomy Performance on C4.5 Decision Tree and Random Forest...Health Informatics Conference, August 12-14, 2019, Melbourne, Australia
The paper applies an artificial intelligence centered method to classify 12 clinical safety incident (CSI) classes. The paper aims to establish a taxonomy that classifies the CSI reports into their correct classes automatically and with high accuracy. The study investigates feasibility of applying t...
| Publicado en: | Studies in Health Technology & Informatics Vol. 266; pp. 83 - 89 |
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| Autores principales: | , , |
| Formato: | algorithm proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2019
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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=138928239&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138928239 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2019 vid: 266 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 138928239 138928239 138928239 10.3233/SHTI190777 138928239 ppf: 83 ppct: 6 formats: tig: atl: Clinical Safety Incident Taxonomy Performance on C4.5 Decision Tree and Random Forest...Health Informatics Conference, August 12-14, 2019, Melbourne, Australia aug: au: GUPTA, Jaiprakash PATRICK, Jon POON, Simon affil: School of Computer Sciences, Sydney University, NSW, Australia. bHealth Language Analytics, Eveleigh, NSW, Australia sug: subj: Decision Trees Algorithms Artificial Intelligence Patient Safety Classification Incident Reports Human Congresses and Conferences Victoria Victoria World Health Organization Data Mining Machine Learning Health Information Management ab: The paper applies an artificial intelligence centered method to classify 12 clinical safety incident (CSI) classes. The paper aims to establish a taxonomy that classifies the CSI reports into their correct classes automatically and with high accuracy. The study investigates feasibility of applying the C4.5 decision tree (DT) classifier and the random forest (RF) classifier for this purpose. The classifiers were trained using randomly selected 3600 CSIs from an Incident Information Management System (IIMS) used by seven hospitals. The taxonomies investigated were the Generic Reference Model (GRM) and the World Health Organization (WHO) patient safety classification. The classifiers trained 13 GRM CSI classes and 9 WHO CSI classes using a bag-of-words approach. The overall taxonomies performance on the RF classifier was better than on the DT classifier. The performance achieved by the classifier applying the WHO taxonomy was better than the GRM taxonomy. Four of the five poorly performing classes in the GRM taxonomy significantly improved their performance on changing the taxonomy. To improve the WHO taxonomy performance the improved WHO (WHO-I) taxonomy was built by adding a new class that did not exist in WHO but existed in GRM. The performance of the RF classifier applied to the WHO-I taxonomy further improved. pubtype: Academic Journal doctype: algorithm proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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