Positioning technology is important to the development of location‐based services. This work proposes a highly accurate and efficient indoor positioning system that uses visible light communication technology. The system uses a fingerprinting positioning mechanism that is based on a mobile device. A dynamic k‐NN (DKNN) mechanism is developed to improve upon k‐nearest neighbor (k‐NN) positioning, which is based on fingerprinting. This work also proposes an integrated angle of arrival (AOA) and fingerprinting mechanism for three‐dimensional positioning, which reduces the time that would otherwise be required for fingerprinting training and improves the accuracy of the indoor positioning system. When used for fixed‐height two‐dimensional positioning, the mean error distance of the DKNN fingerprinting positioning mechanism is approximately 0.15 cm. The AOA mean error distance is approximately 11.43 cm. The traditional k‐NN mean error distance is approximately 1.47 cm. Ignoring the height error, the integrated AOA and fingerprinting positioning mechanisms have a mean error distance of approximately 2.43 cm; the traditional AOA mean error distance is approximately 11.10 cm, and the fingerprinting mean error distance is approximately 2.74 cm. When the height error is considered, the AOA and fingerprinting fusion positioning mechanisms have a mean error distance of approximately 2.83 cm; the AOA mean error distance is approximately 11.62 cm, and the fingerprinting mean error distance is approximately 4.15 cm. The experimental results therefore show that the proposed mechanism provides the highest positioning accuracy.
Intelligent models for predicting hemodialysis-related complications, i.e., hypotension and the deterioration of the quality or obstruction of the AV fistula, based on machine learning (ML) methods were established to offer early warnings to medical staff and give them enough time to provide pre-emptive treatment. Methods: A novel integration platform collected data from the Internet of Medical Things (IoMT) at a dialysis center and inspection results from electronic medical records (EMR) to train ML algorithms and build models. The selection of the feature parameters was implemented using Pearson's correlation method. Then, the eXtreme Gradient Boost (XGBoost) algorithm was chosen to create the predictive models and optimize the feature choice. 75% of collected data are used as a training dataset and the other 25% are used as a testing dataset. Results: We adopted the prediction precision and recall rate of hypotension and AV fistula obstruction to measure the effectiveness of the predictive models. These rates were sufficiently high at approximately 71%-90%. Conclusion: In the context of hemodialysis, hypotension and the deterioration of the quality or obstruction of the arteriovenous (AV) fistula affect treatment quality and patient safety and may lead to a poor prognosis. Our prediction models with high accuracies can provide excellent references and signals for clinical healthcare service providers. Clinical and Translational Impact Statement: With the integrated dataset collected from IoMT and EMR, the superior predictive results of our models for complications of hemodialysis patients are demonstrated. We believe, after enough clinical tests are implemented as planned, these models can assist the healthcare team in making appropriate preparations in advance or adjusting the medical procedures to avoid these adverse events.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.