Title :

Outlier detection for patient monitoring and alerting

Author(s) :

Hauskrecht, Milos [Auteur]
Department of Computer Science - University of Pittsburgh
Batal, Iyad [Auteur]
Department of Computer Science - University of Pittsburgh
Valko, Michal [Auteur]
Sequential Learning [SEQUEL]
Department of Computer Science - University of Pittsburgh
Visweswaran, Shyam [Auteur]
Cooper, Gregory F [Auteur]
Clermont, Gilles [Auteur]

Journal title :

Journal of Biomedical Informatics

Pages :

47-55

Publisher :

Elsevier

Publication date :

2013-02-21

ISSN :

1532-0464

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]
Sciences du Vivant [q-bio]/Médecine humaine et pathologie

English abstract : [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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL