Issue |
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
|
|
---|---|---|
Article Number | 03042 | |
Number of page(s) | 7 | |
Section | Computing | |
DOI | https://doi.org/10.1051/itmconf/20214003042 | |
Published online | 09 August 2021 |
Covid-19 emergency services and disease prediction system
1 Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, India
2 Mentor from Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, India
A medical emergency can be referred to as a medical or behavioral condition, which occurs suddenly and has severe symptoms, including severe pain, such that if a person delays medical attention it can cause: (1) loss of life;(2) serious impairment to the person’s body; or (3) serious damage. Admitting a patient to a healthcare is a complex process which should be managed efficiently, which otherwise may cause serious consequences and patient dissatisfaction. The registration aspect of a patient admission is tedious and cumbersome, which is not at all suitable during a medical emergency. There is a need of a system through which user could fill the form for getting admitted to the hospital beforehand in order prevent delay in treatment. After the registration, the goal is to create a web application for hospital staff to manage the patients data. The web application also analyses the types of patients in particular hospital and represent the data in the form of charts. The implementation of this system is carried out with the help of machine learning algorithms which also analyze Covid data and represent it continent wise, predict future cases in India, and conduct Covid detection by chest scan of a patient.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.