title: Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods.
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.
  
  
  
  We used linear and non-linear kernel methods individually for each data source, and leveraging the powerful multiple kernels for their effective combination. To validate the system, we used data from the EHR of the gastrointestinal department at a university hospital.
  
  
  
  We first investigated the early prediction performance from each data source separately, by computing Area Under the Curve values for processed free text (0.83), blood tests (0.74), and vital signs (0.65), respectively. When exploiting the heterogeneous data sources combined using the composite kernel framework, the prediction capabilities increased considerably (0.92). Finally, posterior probabilities were evaluated for risk assessment of patients as an aid for clinicians to raise alertness at an early stage, in order to act promptly for avoiding AL complications.
  
  
  
  Machine-learning statistical model from EHR data can be useful to predict surgical complications. The combination of EHR extracted free text, blood samples values, and patient vital signs, improves the model performance. These results can be used as a framework for preoperative clinical decision support.
authors.affiliation: Dept. of Signal Theory and Communications, Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada, Spain. Electronic address: cristina.soguero@urjc.es.
authors.forename: Cristina
authors.initials: C
authors.lastname: Soguero-Ruiz
authors.affiliation: Dept. Mathematics and Statistics, University of Tromsø (UiT), Tromsø, Norway.
authors.forename: Kristian
authors.initials: K
authors.lastname: Hindberg
authors.affiliation: Dept. of Signal Theory and Communications, Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada, Spain.
authors.forename: Inmaculada
authors.initials: I
authors.lastname: Mora-Jiménez
authors.affiliation: Dept. of Signal Theory and Communications, Telematics and Computing, Universidad Rey Juan Carlos, Fuenlabrada, Spain.
authors.forename: José Luis
authors.initials: JL
authors.lastname: Rojo-Álvarez
authors.affiliation: Norwegian Centre for Integrated Care and Telemedicine, Norway; University Hospital of North Norway (UNN), Norway; IBM T.J. Watson Research Center, Yorktown Heights, NY, USA.
authors.forename: Stein Olav
authors.initials: SO
authors.lastname: Skrøvseth
authors.affiliation: Dept. Mathematics and Statistics, University of Tromsø (UiT), Tromsø, Norway.
authors.forename: Fred
authors.initials: F
authors.lastname: Godtliebsen
authors.affiliation: Dept. of Gastrointestinal Surgery, UNN, Tromsø, Norway; Institute of Clinical Medicine, UiT, Tromsø, Norway.
authors.forename: Kim
authors.initials: K
authors.lastname: Mortensen
authors.affiliation: Dept. of Gastrointestinal Surgery, UNN, Tromsø, Norway; Clinic for Surgery, Cancer and Women's Health, UNN, Tromsø, Norway.
authors.forename: Arthur
authors.initials: A
authors.lastname: Revhaug
authors.affiliation: Dept. of Gastrointestinal Surgery, UNN, Tromsø, Norway; Institute of Clinical Medicine, UiT, Tromsø, Norway.
authors.forename: Rolv-Ole
authors.initials: RO
authors.lastname: Lindsetmo
authors.affiliation: Norwegian Centre for Integrated Care and Telemedicine, Norway; Dept. of Surgery, Hammerfest Hospital, Norway; Dept. of Colorectal Surgery, University Hospitals Case Medical Center, Cleveland, USA; Institute of Clinical Medicine, UiT, Tromsø, Norway.
authors.forename: Knut Magne
authors.initials: KM
authors.lastname: Augestad
authors.affiliation: Norwegian Centre for Integrated Care and Telemedicine, Norway; Dept. of Physics and Technology, UiT, Tromsø, Norway.
authors.forename: Robert
authors.initials: R
authors.lastname: Jenssen
mesh_terms: D057868:Anastomotic Leak; D003106:Colon / Q000601:surgery; D013505:Digestive System Surgical Procedures; D057286:Electronic Health Records; D006801:Humans; D015233:Models, Statistical; D011183:Postoperative Complications; D012007:Rectum / Q000601:surgery; D018570:Risk Assessment; D060388:Support Vector Machine
keywords: Clinical decision support; Colorectal cancer; Electronic health records; Feature selection; Heterogeneous clinical data; Kernel methods