RT Journal Article
T1 IoMT innovations in diabetes management: Predictive models using wearable data
A1 Rodríguez-Rodríguez, Ignacio
A1 Campo-Valera, María
A1 Rodríguez, José-Víctor
A1 Woo, Wai Lok
K1 Diabetes
K1 Internet de los objetos
K1 Inteligencia artificial - Aplicaciones médicas
K1 Medicina - Procesos de datos
AB Diabetes Mellitus (DM) represents a metabolic disorder characterized by consistently elevated blood glucose levels due to inadequate pancreatic insulin production. Type 1 DM (DM1) constitutes the insulin-dependent manifestation from disease onset. Effective DM1 management necessitates daily blood glucose monitoring, pattern recognition, and cognitive prediction of future glycemic levels to ascertain the requisite exogenous insulin dosage. Nevertheless, this methodology may prove imprecise and perilous. The advent of groundbreaking developments in information and communication technologies (ICT), encompassing Big Data, the Internet of Medical Things (IoMT), Cloud Computing, and Machine Learning algorithms (ML), has facilitated continuous DM1 management monitoring. This investigation concentrates on IoMT-based methodologies for the unbroken observation of DM1 management, thereby enabling comprehensive characterization of diabetic individuals. Integrating machine learning techniques with wearable technology may yield dependable models for forecasting short-term blood glucose concentrations. The objective of this research is to devise precise person-specific short-term prediction models, utilizing an array of features. To accomplish this, inventive modeling strategies were employed on an extensive dataset comprising glycaemia-related biological attributes gathered from a large-scale passive monitoring initiative involving 40 DM1 patients. The models produced via the Random Forest approach can predict glucose levels within a 30-minute horizon with an average error of 18.60 mg/dL for six-hour data, and 26.21 mg/dL for a 45-minute prediction horizon. These findings have also been corroborated with data from 10 Type 2 DM patients as a proof of concept, thereby demonstrating the potential of IoMT-based methodologies for continuous DM monitoring and management.
PB Elsevier
YR 2023
FD 2023-10-09
LK https://hdl.handle.net/10630/29517
UL https://hdl.handle.net/10630/29517
LA eng
NO Ignacio Rodríguez-Rodríguez, María Campo-Valera, José-Víctor Rodríguez, Wai Lok Woo, IoMT innovations in diabetes management: Predictive models using wearable data, Expert Systems with Applications, Volume 238, Part C, 2024, 121994, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2023.121994.
NO Funding for open Access charge: Universidad de Málaga / CBUA.Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI), Junta de Andalucía, Spain. María Campo-Valera is grateful for postdoctoral program Margarita Salas – Spanish Ministry of Universities (financed by European Union – NextGenerationEU). The authors would like to acknowledge project PID2022-137461NB-C32 financed by MCIN/AEI/10.13039/501100011033/FEDER(“Una manera de hacer Europa”), EU.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 22 ene 2026