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@DL4mHealth

Deep Learning for Mobile Health Lab

Deep Learning For Mobile Health Lab

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Welcome to the Deep Learning For Mobile Health (DL4mHealth) Research Lab. We are committed to advancing the field of mobile health through the application of cutting-edge deep learning techniques. Our mission is to develop innovative solutions that harness the power of mobile devices and deep learning to improve healthcare access, delivery, and outcomes.

At DL4mHealth, our research focuses on four primary areas:

  • Medical Time Series: We develop deep learning models to analyze medical time series data, such as physiological signals and vital signs, to enable accurate and real-time detection of health anomalies, prediction of disease progression, and personalized treatment recommendations. By leveraging mobile devices and wearable sensors, we aim to empower patients and healthcare providers with actionable insights for better management of chronic conditions and overall health.

  • Brain Signals: Our work in this domain involves the application of deep learning techniques to Electroencephalography (EEG) data for the identification and analysis of brain activity patterns. We seek to advance the understanding of various neurological conditions, enhance the diagnosis and treatment of brain-related disorders, and promote the development of brain-computer interfaces for improved communication and control.

  • Knowledge Graphs: The DL4mHealth lab also explores the use of graph neural networks (GNNs) to exploit knowledge graphs and further represent complex medical information and relationships. By constructing and refining these graphs using deep learning algorithms, we aim to uncover hidden patterns, facilitate information retrieval, and enable more efficient decision-making processes in healthcare. By applying GNNs, we aim to enhance the predictive performance of our models and contribute to the development of personalized medicine and targeted interventions.

At DL4mHealth, we believe in the transformative potential of deep learning and mobile health technologies to revolutionize healthcare. Our interdisciplinary team of researchers is dedicated to conducting groundbreaking research, fostering collaborations, and promoting knowledge dissemination in order to drive real-world impact and improve the lives of individuals worldwide.

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  1. Contrastive-Learning-in-Medical-Time-Series-Survey Contrastive-Learning-in-Medical-Time-Series-Survey Public

    A Systematic Review: Self-Supervised Contrastive Learning for Medical Time Series

    Jupyter Notebook 8 1

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