title: Realization of a service for the long-term risk assessment of diabetes-related complications.
abstract: OBJECTIVE
  We present a computerized system for the assessment of the long-term risk of developing diabetes-related complications.
  
  
  METHODS
  The core of the system consists of a set of predictive models, developed through a data-mining/machine-learning approach, which are able to evaluate individual patient profiles and provide personalized risk assessments. Missing data is a common issue in (electronic) patient records, thus the models are paired with a module for the intelligent management of missing information.
  
  
  RESULTS
  The system has been deployed and made publicly available as Web service, and it has been fully integrated within the diabetes-management platform developed by the European project REACTION. Preliminary usability tests showed that the clinicians judged the models useful for risk assessment and for communicating the risk to the patient. Furthermore, the system performs as well as the United Kingdom Prospective Diabetes Study (UKPDS) Risk Engine when both systems are tested on an independent cohort of UK diabetes patients.
  
  
  CONCLUSIONS
  Our work provides a working example of risk-stratification tool that is (a) specific for diabetes patients, (b) able to handle several different diabetes related complications, (c) performing as well as the widely known UKPDS Risk Engine on an external validation cohort.
authors.affiliation: Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece. Electronic address: vlagani@ics.forth.gr.
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Lagani
authors.affiliation: Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece.
authors.forename: Franco
authors.initials: F
authors.lastname: Chiarugi
authors.affiliation: Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece.
authors.forename: Dimitris
authors.initials: D
authors.lastname: Manousos
authors.affiliation: Department of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, United Kingdom.
authors.forename: Vivek
authors.initials: V
authors.lastname: Verma
authors.affiliation: Chorleywood Health Center, Chorleywood, United Kingdom.
authors.forename: Joanna
authors.initials: J
authors.lastname: Fursse
authors.affiliation: Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece.
authors.forename: Kostas
authors.initials: K
authors.lastname: Marias
authors.affiliation: Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece; Department of Computer Science, University of Crete, Heraklion, Greece.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Tsamardinos
mesh_terms: D001499:Bayes Theorem; D003131:Combined Modality Therapy; D057225:Data Mining; D003658:Decision Making, Computer-Assisted; D048909:Diabetes Complications / Q000453:epidemiology* / Q000517:prevention & control; D003922:Diabetes Mellitus, Type 1 / Q000150:complications* / Q000628:therapy; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000628:therapy; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D020407:Internet; D000069550:Machine Learning; D008297:Male; D008954:Models, Biological; D057285:Precision Medicine; D018570:Risk Assessment; D012307:Risk Factors
keywords: Clinical decision support systems; DCCT / EDIC studies; Diabetes complications; Machine learning; Risk assessment models