Enabling Healthcare 4.0 for Pandemics最新文献

英文中文

Emerging Technologies for COVID‐19 COVID - 19的新兴技术

Enabling Healthcare 4.0 for Pandemics

Pub Date : 2021-09-21 DOI: 10.1002/9781119769088.ch9

Rohit Anand, Nidhi Sindhwani, Avinash Saini, Shubham

引用次数: 7

A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling 智能护士调度的混合元启发式算法

Enabling Healthcare 4.0 for Pandemics

Pub Date : 2021-09-21 DOI: 10.1002/9781119769088.ch11

T. Pham, S. Dao

引用次数: 1

Rapid Forecasting of Pandemic Outbreak Using Machine Learning: The Case of COVID‐19 使用机器学习快速预测大流行爆发:以COVID - 19为例

Enabling Healthcare 4.0 for Pandemics

Pub Date : 2021-09-21 DOI: 10.1002/9781119769088.ch5

Nishant Jha, D. Prashar

Some huge scope outside impact pandemics has risen in the course of the most recent two decades, including human, natural life, and plant plagues. Authorities face strategy issues that are reliant on deficient information and require sickness gauges. In this manner, there is an earnest need to create models that empower us to outline all accessible information to estimate and screen an advancing pandemic in an ideal way. This chapter targets assessing different models and proposing an early-cautioning AI approach that can conjecture potential flare-ups of ailments. For gauge COVID-19 episodes, the SEIR model, molecule channel calculation and an assortment of pandemic-related datasets are utilized to investigate different models and strategies. In this chapter, various intermediaries have been clarified for the pandemic season prompting comparative conduct of the powerful multiplication number. We found that a solid relationship exists among conferences and analyzed datasets, particularly when considering time based models. Singular parameters gave like distinctive episode seasons esteems, in this way offering an open door for future flare-ups to utilize such data. © 2021 Scrivener Publishing LLC.

在最近二十年中，一些巨大的外部影响流行病已经上升，包括人类、自然生命和植物的瘟疫。当局面临的战略问题依赖于缺乏信息和需要疾病量表。以这种方式，迫切需要创建模型，使我们能够概述所有可获得的信息，以便以理想的方式估计和筛选正在蔓延的大流行病。本章的目标是评估不同的模型，并提出一种早期预警的人工智能方法，可以推测潜在的疾病突发。对于测量COVID-19发作，使用SEIR模型，分子通道计算和各种与大流行相关的数据集来研究不同的模型和策略。在本章中，对大流行季节的各种中介进行了澄清，促进了强大乘法数的比较行为。我们发现会议和分析数据集之间存在牢固的关系，特别是在考虑基于时间的模型时。单一参数给出了不同的剧集季值，这样就为未来的突发事件利用这些数据打开了一扇门。©2021 Scrivener Publishing LLC。

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引用次数: 0

Prevalence of Internet of Things in Pandemic 物联网在流行病中的流行

Enabling Healthcare 4.0 for Pandemics

Pub Date : 2021-09-21 DOI: 10.1002/9781119769088.ch13

Rishita Khurana, Madhulika Bhatia

The current COVID-19 pandemic has created several problems now-a-days. All these problems point to the inability to examine and scale the situation according to the intensity of the outbreak. Along with creating problems, this pandemic has exceeded many boundaries such as the provincial, radical, conceptual, spiritual, social, and educational. It has changed the fundamental nature of our society. For controlling such a pandemic situation, a collection, analysis, elucidation of data on regular basis regarding the spreading disease trends should be done in order to predict the outbreak of major health related symptoms. A proper surveillance is required to cure any type of disease. Surveillance-collection of data can be performed best with the assistance of technology like Internet of things. As “Internet of things is an idea which comprises of interconnected frameworks which has the ability of finding data about a specific thing with the assistance of extraordinary identifiers and detecting capability.” The technological platform of IoT connected with the healthcare system is helpful in observing the infected people. This monitoring is done by the interconnected network offered by IoT [1]. Implementation of such a technology will help to decrease the healthcare expenses and enhance treatment of the infected patients. © 2021 Scrivener Publishing LLC.

当前的COVID-19大流行如今造成了几个问题。所有这些问题都表明，无法根据疫情的严重程度审查和衡量局势。在造成问题的同时，这次大流行已经超越了省、激进、观念、精神、社会和教育等许多界限。它改变了我们社会的基本性质。为了控制这种大流行病，应定期收集、分析和阐明有关疾病传播趋势的数据，以便预测与健康有关的主要症状的爆发。要治愈任何类型的疾病，都需要适当的监测。在物联网等技术的辅助下，数据的监控收集可以得到最好的执行。因为“物联网是一个概念，它由相互连接的框架组成，这些框架能够在非凡的标识符和检测能力的帮助下找到有关特定事物的数据。”物联网与医疗系统连接的技术平台有助于观察感染者。这种监控是通过物联网提供的互联网络来完成的[1]。实施这种技术将有助于减少医疗费用，并加强对受感染患者的治疗。©2021 Scrivener Publishing LLC。

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引用次数: 0

Analysis and Prediction on COVID‐19 Using Machine Learning Techniques 基于机器学习技术的COVID - 19分析与预测

Enabling Healthcare 4.0 for Pandemics

Pub Date : 2021-09-21 DOI: 10.1002/9781119769088.ch3

Supriya Raheja, Shaswat Datta

This paper presents an analysis and prediction of COVID-19 data using machine learning techniques. The present work discusses different machine learning techniques namely linear regression, logistic regression, random forest, and decision tree. The outbreak COVID-19 has attracted the attention of all researchers only on the corona virus. To focus on COVID-19, the present study attempts to analyze COVID-19 data using all machine learning techniques. The work also introduced a decision-making process for further prediction. The techniques are compared with respect to accuracy of prediction. © 2021 Scrivener Publishing LLC.

本文介绍了使用机器学习技术对COVID-19数据的分析和预测。目前的工作讨论了不同的机器学习技术，即线性回归，逻辑回归，随机森林和决策树。COVID-19的爆发吸引了所有研究人员对冠状病毒的关注。为了关注COVID-19，本研究试图使用所有机器学习技术分析COVID-19数据。这项工作还引入了进一步预测的决策过程。在预测精度方面对这些技术进行了比较。©2021 Scrivener Publishing LLC。

引用次数: 0

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