title: Data-driven Temporal Prediction of Surgical Site Infection.
abstract: Analysis of data from Electronic Health Records (EHR) presents unique challenges, in particular regarding nonuniform temporal resolution of longitudinal variables. A considerable amount of patient information is available in the EHR - including blood tests that are performed routinely during inpatient follow-up. These data are useful for the design of advanced machine learning-based methods and prediction models. Using a matched cohort of patients undergoing gastrointestinal surgery (101 cases and 904 controls), we built a prediction model for post-operative surgical site infections (SSIs) using Gaussian process (GP) regression, time warping and imputation methods to manage the sparsity of the data source, and support vector machines for classification. For most blood tests, wider confidence intervals after imputation were obtained in patients with SSI. Predictive performance with individual blood tests was maintained or improved by joint model prediction, and non-linear classifiers performed consistently better than linear models.
authors.affiliation: Department of Signal Theory and Communications, Telematics and Computing, Rey Juan Carlos University, Madrid, Spain.
authors.forename: Cristina
authors.initials: C
authors.lastname: Soguero-Ruiz
authors.affiliation: Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
authors.forename: Wang M E
authors.initials: WM
authors.lastname: Fei
authors.affiliation: Department of Physics and Technology, University of Tromsø - The Arctic University of Norway, Tromsø, Norway; Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway.
authors.forename: Robert
authors.initials: R
authors.lastname: Jenssen
authors.affiliation: Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway; Department of Surgery, Hammerfest Hospital, Hammerfest, Norway.
authors.forename: Knut Magne
authors.initials: KM
authors.lastname: Augestad
authors.affiliation: Department of Signal Theory and Communications, Telematics and Computing, Rey Juan Carlos University, Madrid, Spain.
authors.forename: José-Luis Rojo
authors.initials: JL
authors.lastname: Álvarez
authors.affiliation: Department of Signal Theory and Communications, Telematics and Computing, Rey Juan Carlos University, Madrid, Spain.
authors.forename: Inmaculada Mora
authors.initials: IM
authors.lastname: Jiménez
authors.affiliation: Department of Gastrointestinal Surgery, University Hospital of North Norway, Tromsø, Norway; Department of Clinical Medicine, University of Tromsø - The Arctic University of Norway, Tromsø, Norway.
authors.forename: Rolv-Ole
authors.initials: RO
authors.lastname: Lindsetmo
authors.affiliation: Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway; Department of Mathematics and Statistics, University of Tromsø - The Arctic University of Norway, Tromsø, Norway.
authors.forename: Stein Olav
authors.initials: SO
authors.lastname: Skrøvseth
mesh_terms: D013505:Digestive System Surgical Procedures; D057286:Electronic Health Records; D006801:Humans; D000069550:Machine Learning; D060388:Support Vector Machine; D013530:Surgical Wound Infection