Titlebook: Digital Health and Wireless Solutions; First Nordic Confere Mariella S?rest?niemi,Pantea Keikhosrokiani,Jarmo Conference proceedings‘‘‘‘‘‘

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忘川河

Conference proceedings‘‘‘‘‘‘‘‘ 2024ort and Medical AI.- Digital Care Pathways.- Novel Sensors and Bioinformatics.- Health Technology Assessment and Impact Evaluation.- Wireless Technologies and Medical Devices...This book is open access...?.

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Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICUearly lactate clearance-directed therapy is associated with decreased mortality; thus, serum lactate levels should be assessed. Monitoring a patient’s vital parameters and repetitive blood analysis may have deleterious effects on the patient and also bring an economic burden. Machine learning and tr

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Computed Tomography Artefact Detection Using Deep Learning—Towards Automated Quality Assurance learning (DL) for automated CT artefact classification. Openly available Head CT data from Johns Hopkins University was used. Three common artefacts (patient movement, beam hardening, and ring artefacts (RAs)) and artefact free images were simulated using 2D axial slices. Simulated data were split

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范圍廣

A Persuasive mHealth Application for Postoperative Cardiac Procedures: Prototype Design and Usabilit.1% increase compared to 2010. Also, some individuals who experience a cardiovascular event will require some form of cardiovascular procedure, such as a pacemaker or implantable cardioverter-defibrillator insertion, aneurysm repair, or heart valve replacement. Mobile health (mHealth) is a valuable

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Virtual Reality in Rehabilitation of Executive Functions in Children (VREALFUN) – Study Protocols fo the availability of rehabilitation services is insufficient, regionally unevenly distributed, and unequal in terms of access to rehabilitation. There is a need for easily accessible services. In this paper, we present the VREALFUN project where the major aim is to develop a novel Virtual Reality (V

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A Distributed Framework for?Remote Multimodal Biosignal Acquisition and?Analysisons correlating with clinical-grade devices, their practicality is limited due to constraints in real-time processing, scalability, and interoperability. Moreover, these studies have seldom explored the combined use of multiple modalities or the integration of various sensors. Addressing these gaps,

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