IECE - Institute of Emerging and Computer Engineers

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    Academic Editor
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    Editor 1
    ORCID
    0000-0001-8736-3418
    Rashid Mirzavand
    University of Alberta, Canada
    Summary
    Dr. Rashid Mirzavand is a dedicated academic and researcher leading the Intelligent Wireless Technology Group at the University of Alberta. As an Assistant Professor in the Department of Electrical and Computer Engineering, he is passionate about fostering an inclusive environment that promotes equity, diversity, and excellence in research and education. His entrepreneurial spirit has led him to co-found and serve as CTO for three companies specializing in smart sensors, near-field measurement, and wireless power transfer technologies. Dr. Mirzavand's research focuses on RF/microwave/mm-wave circuits, sensors, reconfigurable intelligent surfaces, and numerical methods. He is committed to advancing innovation and addressing real-world challenges, with a particular emphasis on promoting diversity and equity in STEM fields. With numerous awards, three granted and nine filed US patents, and over 180 publications to his credit, Dr. Mirzavand is a respected voice in his field. He is a registered member of Alberta's Association of Professional Engineers and Geoscientists.
    Edited Journals
    IECE Transactions on Intelligent Systematics
    IECE Contributions
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    Edited
    1
    Free Access | Research Article | 22 December 2024
    Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis
    by
    Huadong Pang
    ,
    Li Zhou
    ,
    Yiping Dong
    ,
    Peiyuan Chen
    ,
    Dian Gu
    ,
    Tianyi Lyu
    ,
    Hansong Zhang
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 1-13, 2024 | DOI:10.62762/TIS.2025.367320
    Abstract
    In the healthcare sector, the application of deep learning technologies has revolutionized data analysis and disease forecasting. This is particularly evident in the field of diabetes, where the deep analysis of Electronic Health Records (EHR) has unlocked new opportunities for early detection and effective intervention strategies. Our research presents an innovative model that synergizes the capabilities of Bidirectional Long Short-Term Memory Networks-Conditional Random Field (BiLSTM-CRF) with a fusion of XGBoost and Logistic Regression. This model is designed to enhance the accuracy of diabetes risk prediction by conducting an in-depth analysis of electronic medical records data. The fir... More >
    Graphical Abstract
    Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis