Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Architecture and organization of a Platform for diagnostics, therapy and post-covid complications using AI and mobile monitoring
7
Zitationen
6
Autoren
2021
Jahr
Abstract
Infectious diseases accompanied mankind throughout its existence. However, in the 20<sup>th</sup> century, with the implementation od mass vaccination, this problem was partially forgotten. It reappeared at the end of the 2019 with the COVID-19 pandemic. The diseases are associated with high mortality, the main causes of which are: respiratory failure, acute respiratory distress syndrome, thrombotic complications, etc. As many centuries ago, the key to fighting a pandemic is to diagnose patients with infections as quickly as possible, isolate them, and implement treatment procedures. In this paper we propose a Platform supporting medics in the fight against epidemic. Unlike alternative systems, the proposed IT Platform will ultimately cover all areas of fighting against COVID-19, from the diagnosis of infection, through treatment, to rehabilitation of post-disease complications. Like most clinical information systems, the Platform is based on Artificial Intelligence, in particular Federated Learning. Also, unlike known solutions, it uses all available historical data of the patient's health and information from real-time mobile diagnostics, using cellular communication and Internet of Things solutions. Such solutions could be helpful in fighting against any future mass infections.
Ähnliche Arbeiten
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
2020 · 22.618 Zit.
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
2020 · 19.271 Zit.
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
2024 · 14.265 Zit.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
2018 · 8.571 Zit.
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
2021 · 7.187 Zit.