3,418 research outputs found

    Context-aware system for cardiac condition monitoring and management: a survey

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    Health monitoring assists physicians in the decision-making process, which in turn, improves quality of life. As technology advances, the usage and applications of context-aware systems continue to spread across different areas in patient monitoring and disease management. It provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters. In this survey, we consider context-aware systems proposed by researchers for health monitoring and management. More specifically, we investigate different technologies and techniques used for cardiac condition monitoring and management. This paper also propose "mCardiac", an enhanced context-aware decision support system for cardiac condition monitoring and management during rehabilitation

    Measurements, Algorithms, and Presentations of Reality: Framing Interactions with AI-Enabled Decision Support

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    Bringing AI technology into clinical practice has proved challenging for system designers and medical professionals alike. The academic literature has, for example, highlighted the dangers of black-box decision-making and biased datasets. Furthermore, end-users’ ability to validate a system’s performance often disappears following the introduction of AI decision-making. We present the MAP model to understand and describe the three stages through which medical observations are interpreted and handled by AI systems. These stages are Measurement, in which information is gathered and converted into data points that can be stored and processed; Algorithm, in which computational processes transform the collected data; and Presentation, where information is returned to the user for interpretation. For each stage, we highlight possible challenges that need to be overcome to develop Human-Centred AI systems. We illuminate our MAP model through complementary case studies on colonoscopy practice and dementia diagnosis, providing examples of the challenges encountered in real-world settings. By defining Human-AI interaction across these three stages, we untangle some of the inherent complexities in designing AI technology for clinical decision-making, and aim to overcome misalignment between medical end-users and AI researchers and developers

    Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert

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    A major challenge related to caring for patients with chronic conditions is the early detection of exacerbations of the disease. Medical personnel should be contacted immediately in order to intervene in time before an acute state is reached, ensuring patient safety. This paper proposes an approach to an ambient intelligence (AmI) framework supporting real-time remote monitoring of patients diagnosed with congestive heart failure (CHF). Its novelty is the integration of: (i) personalized monitoring of the patients health status and risk stage; (ii) intelligent alerting of the dedicated physician through the construction of medical workflows on-the-fly; and (iii) dynamic adaptation of the vital signs' monitoring environment on any available device or smart phone located in close proximity to the physician depending on new medical measurements, additional disease specifications or the failure of the infrastructure. The intelligence lies in the adoption of semantics providing for a personalized and automated emergency alerting that smoothly interacts with the physician, regardless of his location, ensuring timely intervention during an emergency. It is evaluated on a medical emergency scenario, where in the case of exceeded patient thresholds, medical personnel are localized and contacted, presenting ad hoc information on the patient's condition on the most suited device within the physician's reach

    Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detection

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    The emergence of deep learning has significantly enhanced the analysis of electrocardiograms (ECGs), a non-invasive method that is essential for assessing heart health. Despite the complexity of ECG interpretation, advanced deep learning models outperform traditional methods. However, the increasing complexity of ECG data and the need for real-time and accurate diagnosis necessitate exploring more robust architectures, such as transformers. Here, we present an in-depth review of transformer architectures that are applied to ECG classification. Originally developed for natural language processing, these models capture complex temporal relationships in ECG signals that other models might overlook. We conducted an extensive search of the latest transformer-based models and summarize them to discuss the advances and challenges in their application and suggest potential future improvements. This review serves as a valuable resource for researchers and practitioners and aims to shed light on this innovative application in ECG interpretation

    Machine Learning for Biosensors

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    Biosensors have become increasingly popular as diagnostic tools due to their ability to detect and quantify biological analytes in a wide range of applications. With the growing demand for faster and more reliable biosensing devices, machine learning has become a valuable tool in enhancing biosensor performance. In this report, we review recent progress in the application of machine learning to biosensors. We discuss the potential benefits of using machine learning in biosensors, including improved sensitivity, selectivity, and accuracy. We also discuss the various machine learning techniques that have been applied to biosensors, including data preprocessing, feature extraction, and classification and data analysis models. The potential benefits of machine learning in biosensors are discussed, including the ability to analyze large and complex data sets, to detect subtle changes in biomolecular interactions, and to provide real-time monitoring of biological processes. The challenges associated with the integration of machine learning and biosensors are also addressed, including data availability, sensor performance, and computational requirements. We further highlight the challenges and opportunities for the integration of machine learning and biosensors, including the development of portable and low-cost biosensors, and the use of machine learning algorithms for efficient data analysis. Finally, we provide an outlook on future trends and emerging technologies in the field, including the use of artificial intelligence and deep learning algorithms for biosensors, and the potential for creating a fully autonomous biosensing system

    A Survey on Deep Learning in Medical Image Analysis

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    Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201
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