Vincenzo Lagani1, Franco Chiarugi2, Dimitris Manousos2, Vivek Verma3, Joanna Fursse4, Kostas Marias2, Ioannis Tsamardinos5. 1. Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece. Electronic address: vlagani@ics.forth.gr. 2. Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece. 3. Department of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, United Kingdom. 4. Chorleywood Health Center, Chorleywood, United Kingdom. 5. Institute of Computer Science, Foundation for Research and Technology-Hellas, Heraklion, Greece; Department of Computer Science, University of Crete, Heraklion, Greece.
Abstract
AIM: 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.
AIM: 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 diabetespatients. CONCLUSIONS: Our work provides a working example of risk-stratification tool that is (a) specific for diabetespatients, (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: Dennis H Murphree; Elaheh Arabmakki; Che Ngufor; Curtis B Storlie; Rozalina G McCoy Journal: Comput Biol Med Date: 2018-10-16 Impact factor: 4.589