T1GDUJA: Glucose dataset of a patient with type 1 diabetes mellitus

  1. Gaitán Guerrero, Juan Francisco 1
  2. López Ruiz, José Luis 1
  3. Martínez Cruz, Carmen 1
  4. Espinilla Estévez, Macarena 1
  1. 1 Universidad de Jaén
    info

    Universidad de Jaén

    Jaén, España

    ROR https://ror.org/0122p5f64

Verlag: Zenodo

Datum der Publikation: 2023

Art: Dataset

DOI: 10.5281/ZENODO.10409760 GOOGLE SCHOLAR lock_openOpen Access editor

Zusammenfassung

Background Diabetes is a chronic disease that must be constantly monitored, especially in cases of type 1 and 2 diabetes mellitus. Nowadays, technology is helping society to provide innovative solutions in the field of health through sensors or smart devices. In this field, continuous glucose sensors are a huge advance in the development of artificial intelligence algorithms capable of predicting glucose values or obtaining any type of relevant information to improve the quality of patients' health. Unfortunately few datasets exist in this area. Therefore, this study aims to provide the scientific community with a dataset of a type 1 diabetic patient during the period 2023/09/10 and 2023/11/08 (44 days with data). Data Records The data are recorded in a single file entitled glucose_data.csv. This file establishes a Comma Separated Values (CSV) format. The following characteristics can be found in each row of the dataset: date: establishes the moment at which the glucose level was measured. The field is formatted as follows: "YYYYY-MM-DD HH:MM:SS.ssss" (UTC time zone). svg: glucose levels measured in mg/dL. utcOffset: offset in minutes from the time zone where data were collected (Madrid GMT+1 and GMT+2). The dataset contains a total of 10194 samples with an average of 231 samples per day. A summary of the samples per day can be found in the attached image.

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Geldgeber

  • Ministerio de Ciencia e Innovación
    • Plataforma tecnológica de reconocimiento de actividades de la vida diaria para la valoración funcional de personas mayores (PLATERA)
  • Ministerio de Ciencia e Innovación
    • Análisis Interactivo de Conjuntos de Datos Mediante el Uso de Técnicas de Aprendizaje Automático, Lingüística Computacional y Computación Flexible