Outlier detection for patient monitoring ...
Type de document :
Article dans une revue scientifique
PMID :
Titre :
Outlier detection for patient monitoring and alerting
Auteur(s) :
Hauskrecht, Milos [Auteur]
Department of Computer Science - University of Pittsburgh
Batal, Iyad [Auteur]
Department of Computer Science - University of Pittsburgh
Valko, Michal [Auteur]
Department of Computer Science - University of Pittsburgh
Sequential Learning [SEQUEL]
Visweswaran, Shyam [Auteur]
Cooper, Gregory F [Auteur]
Clermont, Gilles [Auteur]
Department of Computer Science - University of Pittsburgh
Batal, Iyad [Auteur]
Department of Computer Science - University of Pittsburgh
Valko, Michal [Auteur]
Department of Computer Science - University of Pittsburgh
Sequential Learning [SEQUEL]
Visweswaran, Shyam [Auteur]
Cooper, Gregory F [Auteur]
Clermont, Gilles [Auteur]
Titre de la revue :
Journal of Biomedical Informatics
Pagination :
47-55
Éditeur :
Elsevier
Date de publication :
2013-02-21
ISSN :
1532-0464
Discipline(s) HAL :
Statistiques [stat]/Machine Learning [stat.ML]
Sciences du Vivant [q-bio]/Médecine humaine et pathologie
Sciences du Vivant [q-bio]/Médecine humaine et pathologie
Résumé en anglais : [en]
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management ...
Lire la suite >We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25% to 66% for a variety of patient-management actions, with 66% corresponding to the strongest outliers.Lire moins >
Lire la suite >We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25% to 66% for a variety of patient-management actions, with 66% corresponding to the strongest outliers.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://doi.org/10.1016/j.jbi.2012.08.004
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- https://doi.org/10.1016/j.jbi.2012.08.004
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- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3567774/pdf
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