• Smart AI-Based Telemedicine System for COVID-19 : Monograph revised edition

    Smart AI-Based Telemedicine System for COVID-19 : Monograph revised edition

    • Nunung Nurul Qomariyah
    • Deepublish
    • 2022
    • 978-623-02-5232-7
    Sinopsis

    In this book, we demonstrate our research proposing an AI-based telemedicine system to fight COVID-19. This system can reduce direct contact between the health workers and the patients. The telemedicine system also allows two-way communication between the patients and the doctors from a distance. The telemedicine system is equipped with an AI-based module that can help the doctors to gather an initial description of the patient’s condition. We focus on the explainable AI which is able to allow a human interpretation of what is learned through machine learning. The proposed solution is not only building the intelligent telemedicine application but also allowing the medical experts to validate the resulting models, e.g., via suitable visualizations. This feature will become an advantage of our proposed solution when compared to the other existing telemedicine applications on the market.

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Kode Buku : 240287
Kode Klasifikasi : 006.3
Judul Buku : Smart AI-Based Telemedicine System for COVID-19 : Monograph revised edition
Edisi : 1
Penulis : Nunung Nurul Qomariyah
Penerbit : Deepublish
Bahasa : Inggris
Tahun : 2022
ISBN : 978-623-02-5232-7
Tajuk Subjek : telemedicine system
Deskripsi : xvi, 74 hlm.; 23cm
Eksemplar : 2
Stok : 2
Petugas : Rizki Arsika Yunita, S.Hum
In this book, we demonstrate our research proposing an AI-based telemedicine system to fight COVID-19. This system can reduce direct contact between the health workers and the patients. The telemedicine system also allows two-way communication between the patients and the doctors from a distance. The telemedicine system is equipped with an AI-based module that can help the doctors to gather an initial description of the patient’s condition. We focus on the explainable AI which is able to allow a human interpretation of what is learned through machine learning. The proposed solution is not only building the intelligent telemedicine application but also allowing the medical experts to validate the resulting models, e.g., via suitable visualizations. This feature will become an advantage of our proposed solution when compared to the other existing telemedicine applications on the market.
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