Cargando…

Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting

Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to...

Descripción completa

Detalles Bibliográficos
Autores principales: Ehrentraut, Claudia, Ekholm, Markus, Tanushi, Hideyuki, Tiedemann, Jörg, Dalianis, Hercules
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5802538/
https://www.ncbi.nlm.nih.gov/pubmed/27496862
http://dx.doi.org/10.1177/1460458216656471
_version_ 1783298539006197760
author Ehrentraut, Claudia
Ekholm, Markus
Tanushi, Hideyuki
Tiedemann, Jörg
Dalianis, Hercules
author_facet Ehrentraut, Claudia
Ekholm, Markus
Tanushi, Hideyuki
Tiedemann, Jörg
Dalianis, Hercules
author_sort Ehrentraut, Claudia
collection PubMed
description Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7 percent recall, 79.7 percent precision and 85.7 percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.
format Online
Article
Text
id pubmed-5802538
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-58025382018-02-20 Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting Ehrentraut, Claudia Ekholm, Markus Tanushi, Hideyuki Tiedemann, Jörg Dalianis, Hercules Health Informatics J Articles Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7 percent recall, 79.7 percent precision and 85.7 percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task. SAGE Publications 2016-08-04 2018-03 /pmc/articles/PMC5802538/ /pubmed/27496862 http://dx.doi.org/10.1177/1460458216656471 Text en © The Author(s) 2016 http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Articles
Ehrentraut, Claudia
Ekholm, Markus
Tanushi, Hideyuki
Tiedemann, Jörg
Dalianis, Hercules
Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting
title Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting
title_full Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting
title_fullStr Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting
title_full_unstemmed Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting
title_short Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting
title_sort detecting hospital-acquired infections: a document classification approach using support vector machines and gradient tree boosting
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5802538/
https://www.ncbi.nlm.nih.gov/pubmed/27496862
http://dx.doi.org/10.1177/1460458216656471
work_keys_str_mv AT ehrentrautclaudia detectinghospitalacquiredinfectionsadocumentclassificationapproachusingsupportvectormachinesandgradienttreeboosting
AT ekholmmarkus detectinghospitalacquiredinfectionsadocumentclassificationapproachusingsupportvectormachinesandgradienttreeboosting
AT tanushihideyuki detectinghospitalacquiredinfectionsadocumentclassificationapproachusingsupportvectormachinesandgradienttreeboosting
AT tiedemannjorg detectinghospitalacquiredinfectionsadocumentclassificationapproachusingsupportvectormachinesandgradienttreeboosting
AT dalianishercules detectinghospitalacquiredinfectionsadocumentclassificationapproachusingsupportvectormachinesandgradienttreeboosting