ArticleSensors (Basel, Switzerland)2021
A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.
- Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review.BMC medical informatics and decision making · 2025Pooled it
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- AI applications across the cancer pain care Continuum: applications and future directions.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
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- The Digital Transformation of Healthcare Through Intelligent Technologies: A Path Dependence-Augmented-Unified Theory of Acceptance and Use of Technology Model for Clinical Decision Support Systems.Healthcare (Basel, Switzerland) · 2025Article
- Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients.PloS one · 2024Article
- Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance.Diagnostics (Basel, Switzerland) · 2023Review
- Application of Deep Learning for Prediction of Alzheimer's Disease in PET/MR Imaging.Bioengineering (Basel, Switzerland) · 2023Review
- Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care.JMIR medical education · 2023Article
- Medical image analysis using deep learning algorithms.Frontiers in public health · 2023Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the "Internet of Medical Things" (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning.
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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.