ArticleSensors (Basel, Switzerland)2018
A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 244 citations in OpenAlex.
- How Does Medical Artificial Intelligence Revolutionize Physician Productivity?Yonsei medical journal · 2026Pooled it
- The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Deception detection with machine learning: A systematic review and statistical analysis.PloS one · 2023Pooled it
- Recent Advances in Nanocomposite Hydrogel Sensors for Healthcare Applications: Fabrication Techniques, Multifunctionality and Challenges.Nanomaterials (Basel, Switzerland) · 2026Review
- Next-Gen Healthcare Devices: Evolution of MEMS and BioMEMS in the Era of the Internet of Bodies for Personalized Medicine.Micromachines · 2025Review
- Personalized Nutrition in the Era of Digital Health: A New Frontier for Managing Diabetes and Obesity.Food science & nutrition · 2025Review
- ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System.Sensors (Basel, Switzerland) · 2024Article
- A Survey on Data-Driven Approaches for Reliability, Robustness, and Energy Efficiency in Wireless Body Area Networks.Sensors (Basel, Switzerland) · 2024Review
- Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.ACS sensors · 2024Review
- FloatingBlue: A Delay Tolerant Networks-Enabled Internet of Things Architecture for Remote Areas Combining Data Mules and Low Power Communications.Sensors (Basel, Switzerland) · 2024Article
- A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients.Sensors (Basel, Switzerland) · 2023Article
- RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda.Journal of ambient intelligence and humanized computing · 2023Article
- Evaluation of artificial intelligence techniques in disease diagnosis and prediction.Discover artificial intelligence · 2023Review
- NSSI questionnaires revisited: A data mining approach to shorten the NSSI questionnaires.PloS one · 2023Article
- A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications.PloS one · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation.Sensors (Basel, Switzerland) · 2022Article
- Hurdles to developing and scaling remote patients' health management tools and systems: a scoping review.Systematic reviews · 2022Article
- Overview of Artificial Intelligence-Driven Wearable Devices for Diabetes: Scoping Review.Journal of medical Internet research · 2022Article
- Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI).Journal of digital imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning⁻based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.