Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods.
Identifieur interne : 000025 ( Main/Exploration ); précédent : 000024; suivant : 000026Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods.
Auteurs : Cristina Soguero-Ruiz [Espagne] ; Kristian Hindberg [Norvège] ; Inmaculada Mora-Jiménez [Espagne] ; José Luis Rojo-Álvarez [Espagne] ; Stein Olav Skr Vseth [États-Unis] ; Fred Godtliebsen [Norvège] ; Kim Mortensen [Norvège] ; Arthur Revhaug [Norvège] ; Rolv-Ole Lindsetmo [Norvège] ; Knut Magne Augestad [Norvège] ; Robert Jenssen [Norvège]Source :
- Journal of biomedical informatics [ 1532-0480 ] ; 2016.
Abstract
In this work, we have developed a learning system capable of exploiting information conveyed by longitudinal Electronic Health Records (EHRs) for the prediction of a common postoperative complication, Anastomosis Leakage (AL), in a data-driven way and by fusing temporal population data from different and heterogeneous sources in the EHRs.
DOI: 10.1016/j.jbi.2016.03.008
PubMed: 26980235
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">In this work, we have developed a learning system capable of exploiting information conveyed by longitudinal Electronic Health Records (EHRs) for the prediction of a common postoperative complication, Anastomosis Leakage (AL), in a data-driven way and by fusing temporal population data from different and heterogeneous sources in the EHRs.</div>
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