2026-05-27T05:30:27Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/295392026-02-03T11:23:22Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Rodríguez-Rodríguez, Ignacio
author
Campo-Valera, María
author
Rodríguez, José-Víctor
2024-01-31T13:46:24Z
2024-01-31T13:46:24Z
2023-09-22
Ignacio Rodríguez-Rodríguez, María Campo-Valera, José-Víctor Rodríguez, Forecasting glycaemia for type 1 diabetes mellitus patients by means of IoMT devices, Internet of Things, Volume 24, 2023, 100945, ISSN 2542-6605, https://doi.org/10.1016/j.iot.2023.100945.
https://hdl.handle.net/10630/29539
10.1016/j.iot.2023.100945
The chronic metabolic condition, Type 1 diabetes mellitus (DM1), is marked by consistent hyperglycemia due to the body's inability to produce sufficient insulin. This necessitates the patient's daily monitoring of blood glucose fluctuations to discern a trend and predict future glycemia, subsequently dictating the amount of external insulin needed to regulate glycemia effectively. However, this technique often grapples with a degree of inaccuracy, presenting potential hazards. Nonetheless, contemporary advancements in information and communication technologies (ICT) coupled with novel biological signal sensors offer a refreshing perspective for DM1 management by enabling comprehensive, continual patient health evaluation. Herein, burgeoning technological disruptions such as Big Data, the internet of medical things (IoMT), cloud computing, and machine learning algorithms (ML) could serve pivotal roles in the effective control of DM1. This paper delves into the exploration of the latest IoMT-based methodologies for the unbroken surveillance of DM1 management, facilitating a profound characterization of diabetic patients. The fusion of wearable technologies with machine learning strategies has the potential to yield robust models for short-term blood glucose prediction. The ambition of this study is to develop precise, individual-centric prediction models harnessing an array of pertinent factors. The study applied modeling techniques to a comprehensive dataset comprising glycaemia-associated biological attributes, sourced from an expansive passive monitoring campaign involving 40 DM1 patients. Leveraging the Random Forest method, the resulting models can predict glucose levels over a 30-min time span with an average error as minimal as 18.60 mg/dL for six-hour data and 26.21 mg/dL for a 45-minute prediction horizon, offering also a good performance in the prediction delay.
spa
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Diabetes
Internet de los objetos
Inteligencia artificial - Aplicaciones médicas
Medicina - Procesos de datos
Forecasting glycaemia for type 1 diabetes mellitus patients by means of IoMT devices
journal article
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