Memorias de investigación
Research Publications in journals:
A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs

Research Areas
  • Artificial intelligence (neuronal nets, expert systems, etc),
  • Cronic diseases (diabetes, asthma, others),
  • Medical computing,
  • Telemedicine

Background The growth of diabetes prevalence is causing an increasing demand in health care services which affects the clinicians? workload as medical resources do not grow at the same rate as the diabetic population. Decision support tools can help clinicians with the inspection of monitoring data, providing a preliminary analysis to ease their interpretation and reduce the evaluation time per patient. This paper presents Sinedie, a clinical decision support system designed to manage the treatment of patients with gestational diabetes. Sinedie aims to improve access to specialized healthare assistance, to prevent patients from unnecessary displacements, to reduce the evaluation time per patient and to avoid gestational diabetes adverse outcomes. Methods A web-based telemedicine platform was designed to remotely evaluate patients allowing them to upload their glycaemia data at home directly from their glucose meter, as well as report other monitoring variables like ketonuria and compliance to dietary treatment. Glycaemia values, not tagged by patients, are automatically labelled with their associated meal by a classifier based on the Expectation Maximization clustering algorithm and a C4.5 decision tree learning algorithm. Two finite automata are combined to determine the patient?s metabolic condition, which is analysed by a rule-based knowledge base to generate therapy adjustment recommendations. Diet recommendations are automatically prescribed and notified to the patients, whereas recommendations about insulin requirements are notified also to the physicians, who will decide if insulin needs to be prescribed. The system provides clinicians with a view where patients are prioritized according to their metabolic condition. A randomized controlled clinical trial was designed to evaluate the effectiveness and safety of Sinedie interventions versus standard care and its impact in the professionals? workload in terms of the clinician?s time required per patient; number of face-to-face visits; frequency and duration of telematics reviews; patients? compliance to self-monitoring; and patients? satisfaction. Results Sinedie was clinically evaluated at ?Parc Tauli University Hospital? in Spain during 17 months with the participation of 90 patients with gestational diabetes. Sinedie detected all situations that required a therapy adjustment and all the generated recommendations were safe. The time devoted by clinicians to patients? evaluation was reduced by 27.389% and face-to-face visits per patient were reduced by 88.556%. Patients reported to be highly satisfied with the system, considering it useful and trusting in being well controlled. There was no monitoring loss and, in average, patients measured their glycaemia 3.890 times per day and sent their monitoring data every 3.477 days. Conclusions Sinedie generates safe advice about therapy adjustments, reduces the clinicians' workload and helps physicians to identify which patients need a more urgent or more exhaustive examination and those who present good metabolic control. Additionally, Sinedie saves patients unnecessary displacements which contributes to medical centres? waiting list reduction
International Journal of Medical Informatics
Impact factor JCR
Impact info
ISI - 2016
Journal number
From page
To page
Computer Science, Information Systems: 29/146; Health Care Sciences & Services: 18/90; Medical Informatics: 5/23;
  • Autor: Estefanía Caballero Ruíz UPM
  • Autor: Gema Garcia Saez UPM
  • Autor: Mercedes Rigla Cros Hospital Universitario Parc Taulí de Sabadell
  • Autor: María Villaplana Hospital Universitario Parc Taulí de Sabadell
  • Autor: Belén Pons Hospital Universitario Parc Taulí de Sabadell
  • Autor: Maria Elena Hernando Perez UPM

Research Group, Departaments and Institutes related
  • Creador: Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB