1. Proceedings
  2. I3M
  3. 2019
  4. IWISH
  5. 10.46354/i3m.2019.iwish.003

Towards an adaptive decision-support system for Type I Diabetes treatment based on simulation and machine learning

  • a Iván Castilla-Rodríguez  , 
  • b Rafael Arnay  , 
  • c José M. González-Caval  , 
  • d Juan A. Méndez  , 
  • e Amado Rivero-Santana  , 
  • f Lidia García-Pérez  
  • a,b,c,dDepartamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, Spain
  • a,e,f Research Network on Health Services for Chronic Conditions (REDISSEC) Carlos III Health Institute, Madrid, Spain
  • e,f Fundación Canaria de Investigación Sanitaria (FUNCANIS), Tenerife, Spain
Cite as
Castilla-Rodríguez I., Arnay R., González-Cava J. M., Méndez J. A., Rivero-Santana A., García-Pérez L. (2019). Towards an adaptive decision-support system for Type I Diabetes treatment based on simulation and machine learning. Proceedings of the 8th International Workshop on Innovative Simulation for Healthcare (IWISH 2019), pp. 15-21. DOI: https://doi.org/10.46354/i3m.2019.iwish.003

Abstract

Diabetes is one of the most prevalent chronic diseases in the world, especially in middle- and low-income countries. Inter- and intra-patient variability greatly hinders the establishment of effective treatments by clinicians, even among those most experienced. This variability also prevents health administrations to establish adequate controls that guarantee the application of the most cost-effective interventions. In this work, we propose a decision support system that uses simulation and machine learning as tools to provide the clinician with information adapted to the patient on the best intervention for a patient in terms of effectiveness and cost-effectiveness.

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