For ubiquitous health care systems which continuously monitor a person's vital signs such as electrocardiogram (ECG), body surface temperature and three-dimensional (3D) acceleration by wireless, it is important to accurately detect the occurrence of an abnormal event in the data and immediately inform a medical doctor of its detail. In this paper, we introduce a remote health care system, which is composed of a wireless vital sensor, multiple receivers and a triage engine installed in a desktop personal computer (PC). The middleware installed in the receiver, which was developed in C++, supports reliable data handling of vital data to the ethernet port. On the other hand, the human interface of the triage engine, which was developed in JAVA, shows graphics on his/her ECG data, 3D acceleration data, body surface temperature data and behavior status in the display of the desktop PC and sends an urgent e-mail containing the display data to a pre-registered medical doctor when it detects the occurrence of an abnormal event. In the triage engine, the lethal arrhythmia detection algorithm based on short time Fourier transform (STFT) analysis can achieve 100 % sensitivity and 99.99 % specificity, and the behavior recognition algorithm based on the combination of the nearest neighbor method and the Naive Bayes method can achieve more than 71 % classification accuracy.
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http://dx.doi.org/10.1109/IEMBS.2011.6090405 | DOI Listing |
PLOS Digit Health
January 2025
Rwanda Ministry of Health, Kigali, Rwanda.
Community isolation of patients with communicable infectious diseases limits spread of pathogens but our understanding of isolated patients' needs and challenges is incomplete. Rwanda deployed a digital health service nationally to assist public health clinicians to remotely monitor and support SARS-CoV-2 cases via their mobile phones using daily interactive short message service (SMS) check-ins. We aimed to assess the texting patterns and communicated topics to better understand patient experiences.
View Article and Find Full Text PDFAdv Med Educ Pract
January 2025
Department of Mental Health Nursing, Faculty of Nursing, Universitas Padjadjaran, Sumedang, West Java, Indonesia.
Background: Tabletop Disaster Exercise (TDE) is a unique learning method through simulation designed to improve disaster preparedness. It is used every year to train health workers and students in disaster preparedness. However, no review has summarized the potential of TDE.
View Article and Find Full Text PDFEur J Radiol
January 2025
School of Biomedical Engineering & Imaging Sciences, King's College London, London, the United Kingdom of Great Britain and Northern Ireland; Department of Neuroradiology, King's College Hospital National Health Service Foundation Trust, London, the United Kingdom of Great Britain and Northern Ireland. Electronic address:
Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tools can mitigate the delays in reporting scans by flagging time-sensitive and actionable findings. In this study, we aim to investigate current stakeholder perspectives and identify obstacles to integrating AI in clinical pathways.
View Article and Find Full Text PDFBrain Commun
November 2024
Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
Moyamoya is a non-atherosclerotic intracranial steno-occlusive condition that places patients at high risk for ischaemic stroke. Randomized trials of surgical revascularization demonstrating efficacy in ischaemic moyamoya have not been performed, and as such, biomarkers of parenchymal haemodynamic impairment are needed to assist with triage and evaluate post-surgical response. In this prospective study, we test the hypothesis that parenchymal cerebrovascular reactivity (CVR) metrics in response to a fixed-inspired 5% carbon dioxide challenge correlate with recent focal ischaemic symptoms.
View Article and Find Full Text PDFPLOS Digit Health
January 2025
Clinical Care & Research, ORTEC B.V., Zoetermeer, The Netherlands.
Precision, or personalised medicine has advanced requirements for medical data management systems (MedDMSs). MedDMS for precision medicine should be able to process hundreds of parameters from multiple sites, be adaptable while remaining in sync at multiple locations, real-time syncing to analytics and be compliant with international privacy legislation. This paper describes the LogiqSuite software solution, aimed to support a precision medicine solution at the patient care (LogiqCare), research (LogiqScience) and data science (LogiqAnalytics) level.
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