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HUPA-UCM diabetes dataset.
Hidalgo, J Ignacio; Alvarado, Jorge; Botella, Marta; Aramendi, Aranzazu; Velasco, J Manuel; Garnica, Oscar.
Afiliación
  • Hidalgo JI; Universidad Complutense de Madrid, Profesor José García Santesmases 9, Madrid, Spain.
  • Alvarado J; Universidad de Extremadura, Avda. de Elvas s/n, Badajoz, Spain.
  • Botella M; Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Spain.
  • Aramendi A; Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Spain.
  • Velasco JM; Universidad Complutense de Madrid, Profesor José García Santesmases 9, Madrid, Spain.
  • Garnica O; Universidad Complutense de Madrid, Profesor José García Santesmases 9, Madrid, Spain.
Data Brief ; 55: 110559, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38948410
ABSTRACT
This dataset provides a collection of Continuous Glucose Monitoring (CGM) data, insulin dose administration, meal ingestion counted in carbohydrate grams, steps, calories burned, heart rate, and sleep quality and quantity assessment ac- quired from 25 people with type 1 diabetes mellitus (T1DM). CGM data was acquired by FreeStyle Libre 2 CGMs, and Fitbit Ionic smartwatches were used to obtain steps, calories, heart rate, and sleep data for at least 14 days. This dataset could be utilized to obtain glucose prediction models, hypoglycemia and hyperglycemia prediction models, and research on the relationships among sleep, CGM values, and the rest of the mentioned variables. This dataset could be used directly from the preprocessed version or customized from raw data. The data set has been used previously with different machine learning algorithms to predict glucose values, hypo, and hyperglycemia and to analyze influences among the features and the quality and quantity of sleep in people with T1DM.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Data Brief Año: 2024 Tipo del documento: Article País de afiliación: España

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Data Brief Año: 2024 Tipo del documento: Article País de afiliación: España