Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
Automatic classification of glycemia measurements to enhance data interpretation in an expert system for gestational diabetes
Áreas de investigación
  • Inteligencia artificial (redes neuronales, lógica borrosa, sistemas expertos, etc),
  • Enfermedades crónicas (diabetes, asma, otras),
  • Telemedicina
Expert systems for diabetes care need to automatically evaluate glycaemia measurements in relationship to meals to correctly determine patients? metabolic condition and generate recommendations about therapy adjustments. Most glucose meters allow patients to manually label each measurement with a meal tag, but as this utility is not always used, a completion procedure is needed. Classification methods are usually based on predefined mealtimes and present insufficient accuracy that might affect the automatic data analysis. Expert systems in diabetes require a reliable method to manage incomplete glycaemia data so that they can determine if patients? metabolic condition is altered due to a specific meal or due to an extended fasting period. This paper presents the design and application of a classification module to automatically assign the appropriate meal and ?moment of measurement? to incomplete glycaemia data. Different machine learning techniques were studied in order to design the best classification algorithm in terms of accuracy. The selected classifier was implemented with a C4.5 decision tree with 7 input features selected with a wrapper evaluator and the genetic search algorithm, which achieved 95.45% of accuracy with the training set on cross-validation. The classification module was integrated in the Sinedie expert system for gestational diabetes care and was evaluated in a clinical environment for 8 months with 42 patients. A total of 7,113 glycaemia measurements were uploaded by patients into the Sinedie system and were completed by the ?classification module?. The 98.79% of the measurements were correctly classified, while patients modified the automatic classification of 1.21% of them. Classification results were improved by 21.04% compared to a classification based on predefined mealtimes. The automatic classification of glycaemia measurements minimizes the patient's intervention, allows structuring measurements in relationship to meals and makes automatic data interpretation by expert systems more reliable.
Título de la revista
Expert Systems With Applications
Factor de impacto JCR
Información de impacto
Journal Impact Factor 2015
Número de revista
Desde la página
Hasta la página
Computer science, artificial intelligence: 19/130; Q1; 85.769 Engineering, electrical & electronic: 25/257; Q1; 89.689 Operation research & management science: 6/82; Q1; 93.293
Esta actividad pertenece a memorias de investigación
  • Autor: Estefanía Caballero Ruíz (UPM)
  • Autor: Gema Garcia Saez (UPM)
  • Autor: Mercedes Rigla Cros (Hospital de Sabadell)
  • Autor: María Villaplana (Hospital de Sabadell)
  • Autor: Belén Pons (Hospital de Sabadell)
  • Autor: Maria Elena Hernando Perez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Bioingeniería y Telemedicina
  • Departamento: Tecnología Fotónica y Bioingeniería
S2i 2022 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)