Towards an ICT-Based Platform for Type 1 Diabetes Mellitus Management

Rodríguez‐Rodríguez, Ignacio; Zamora-Izquierdo, Miguel A.; Rodríguez, José Luis Martín

doi:10.3390/app8040511

Cited by 30 publications

(25 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Rodriguez-Rodriguez et al revealed that recent technologies, such as IoT, big data, cloud computing, novel biosensors, and machine learning can perform significant part in improving the diabetes management [38]. The previous studies have shown significant results with the implementation of IoT in healthcare systems.…”

Section: Managerial Implicationsmentioning

confidence: 99%

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

et al. 2018

View full text Add to dashboard Cite

As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage.

show abstract

Section: Managerial Implicationsmentioning

confidence: 99%

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The possibilities of a remote blood glucose monitoring have also been exposed more recently in [ 78 ]. All of these issues have been specifically analyzed in a previous work of the authors [ 79 ], with an extended evaluation of the historical development of theses ICT-based platforms, their pros and cons, and a proposal of a complete diabetes telemedicine system in an ICT environment.…”

Section: Challenges To Be Overcomementioning

confidence: 99%

Variables to Be Monitored via Biomedical Sensors for Complete Type 1 Diabetes Mellitus Management: An Extension of the “On-Board” Concept

Rodríguez‐Rodríguez

Rodríguez

Zamora-Izquierdo

2018

Journal of Diabetes Research

Self Cite

View full text Add to dashboard Cite

Type 1 diabetes mellitus (DM1) is a growing disease, and a deep understanding of the patient is required to prescribe the most appropriate treatment, adjusted to the patient's habits and characteristics. Before now, knowledge regarding each patient has been incomplete, discontinuous, and partial. However, the recent development of continuous glucose monitoring (CGM) and new biomedical sensors/gadgets, based on automatic continuous monitoring, offers a new perspective on DM1 management, since these innovative devices allow the collection of 24-hour biomedical data in addition to blood glucose levels. With this, it is possible to deeply characterize a diabetic person, offering a better understanding of his or her illness evolution, and, going further, develop new strategies to manage DM1. This new and global monitoring makes it possible to extend the “on-board” concept to other features. This well-known approach to the processing of variable “insulin” describes some inertias and aggregated/remaining effects. In this work, such analysis is carried out along with a thorough study of the significant variables to be taken into account/monitored—and how to arrange them—for a deep characterization of diabetic patients. Lastly, we present a case study evaluating the experience of the continuous and comprehensive monitoring of a diabetic patient, concluding that the huge potential of this new perspective could provide an acute insight into the patient's status and extract the maximum amount of knowledge, thus improving the DM1 management system in order to be fully functional.

show abstract

“…Given that the possibilities of ICT have been expanded by the Internet of Things (IoT) [4], new tools have emerged to manage and prevent the incidence of intimate partner and other types of gender-based violence. However, even though biosensors and other resources have had positive outcomes in healthcare [5], challenges remain to their use among IPV survivors seeking protection. In particular, previous trials have focused on finding means by which a victim of assault can set off an alarm; however, given the speed and unpredictability which characterize acts of IPV, most women do not have the chance to consciously activate a safety device.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

et al. 2020

Self Cite

View full text Add to dashboard Cite

Intimate Partner Violence (IPV) dramatically compromises the free and complete development of many women around the world, therefore leading to social asymmetry regarding the right to personal safety. In many cases, a woman who has reported her partner to police for gender-based violence needs to ensure her protection (either before the trial of the aggressor or after their freedom). Thus, it would be ideal if autonomous alarm systems could be developed in order to call the police if necessary. Up to now, many proposals have been presented in this regard, including solutions based on Information and Communication Technologies (ICT) but, unfortunately, these approaches usually rely on the active participation of the victims (survivors), who have to turn the system on by themselves if needed. Therefore, in order to overcome such limitations, in this work, a passive continuous monitoring system is proposed which uses biosensors attached to the survivor as well as machine learning techniques to infer if an abnormal situation related to gender-based violence is taking place, activating in this case an alarm. The monitoring structure of the system supervises a great deal of bio-signals according to the current status of technology of wearables and biomedical devices. The presented biosensors-based surveillance solution can also be manually disconnected for 30/60/90 min (on demand) in order to avoid false positives when a woman is, for example, practicing sports or carrying out other inoffensive activities that could incorrectly activate the alarm.

show abstract

Towards an ICT-Based Platform for Type 1 Diabetes Mellitus Management

Cited by 30 publications

References 50 publications

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Variables to Be Monitored via Biomedical Sensors for Complete Type 1 Diabetes Mellitus Management: An Extension of the “On-Board” Concept

An Autonomous Alarm System for Personal Safety Assurance of Intimate Partner Violence Survivors Based on Passive Continuous Monitoring through Biosensors

Contact Info

Product

Resources

About