IoMT innovations in diabetes management: Predictive models using wearable data

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.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

Forecasting glycaemia for type 1 diabetes mellitus patients by means of IoMT devices

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.Funding for open Access charge: Universidad de Málaga / CBUA