scholarly journals Mapping the landscape of Artificial Intelligence applications against COVID-19

2020 ◽  
Vol 69 ◽  
pp. 807-845 ◽  
Author(s):  
Joseph Bullock ◽  
Alexandra Luccioni ◽  
Katherine Hoffman Pham ◽  
Cynthia Sin Nga Lam ◽  
Miguel Luengo-Oroz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Open Science ◽  
Data Sources ◽  
World Health ◽  
Artificial Intelligence Research ◽  
Non Invasive ◽  
Multiple Data ◽  
The World ◽  
Health Organization

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.

scholarly journals IR 4.0 TECHNOLOGY: JEWEL IN PANDEMIC ERA

2020 ◽  
Vol 5 (21) ◽  
pp. 240-247
Author(s):  
Ahmad Shamsul Abd Aziz ◽  
Nor Azlina Mohd Noor ◽  
Khadijah Mohamed
Keyword(s):  
Artificial Intelligence ◽  
Intellectual Property ◽  
Crisis Management ◽  
World Health ◽  
Human Communication ◽  
Secondary Sources ◽  
Realistic Approach ◽  
Library Research ◽  
The World ◽  
Health Organization

Coronavirus 2019 (COVID 19) was first reported in Wuhan, China in December 2019. The rapidly spreading coronavirus outbreak around the world had forced the World Health Organization (WHO) to declare COVID 19 as a pandemic on March 11, 2020. Crisis management for COVID 19 requires an integrated and realistic approach, and a focus on technology can assist matters to become more efficient. Although IR 4.0 technology is widely used in dealing with pandemic crises, the relevant laws relating to intellectual property laws, especially copyrights and patents with this technology must continue to be protected. This article discusses IR 4.0 technologies such as artificial intelligence (AI) and blockchain as applied in the era of pandemics and intellectual property protection associated with this technology. For this purpose, this article applies library research methodology by analyzing primary and secondary sources. This article concludes that IR 4.0 technology such as artificial intelligence and blockchain is seen as jewels in the era of pandemics because as with the use of this technology, human communication can be reduced. In addition, this technology can also reduce dependence on manpower. Improvements to intellectual property laws can be done in providing more protection against this IR 4.0 technology.

Web-Based Machine Learning Application for Heart Disease Prediction

2022 ◽  
pp. 383-393
Author(s):  
Lokesh M. Giripunje ◽  
Tejas Prashant Sonar ◽  
Rohit Shivaji Mali ◽  
Jayant C. Modhave ◽  
Mahesh B. Gaikwad
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Web Application ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Web Based ◽  
The World ◽  
Comprehensive Survey ◽  
Health Organization

Risk because of heart disease is increasing throughout the world. According to the World Health Organization report, the number of deaths because of heart disease is drastically increasing as compared to other diseases. Multiple factors are responsible for causing heart-related issues. Many approaches were suggested for prediction of heart disease, but none of them were satisfactory in clinical terms. Heart disease therapies and operations available are so costly, and following treatment, heart disease is also costly. This chapter provides a comprehensive survey of existing machine learning algorithms and presents comparison in terms of accuracy, and the authors have found that the random forest classifier is the most accurate model; hence, they are using random forest for further processes. Deployment of machine learning model using web application was done with the help of flask, HTML, GitHub, and Heroku servers. Webpages take input attributes from the users and gives the output regarding the patient heart condition with accuracy of having coronary heart disease in the next 10 years.

scholarly journals AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study

2021 ◽  
Author(s):  
Naser Zaeri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ct Scan ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Related Data ◽  
Recent Developments ◽  
Intelligence Modeling ◽  
Health Organization ◽  
Imaging Algorithms

The coronavirus disease 2019 (COVID-19) outbreak has been designated as a worldwide pandemic by World Health Organization (WHO) and raised an international call for global health emergency. In this regard, recent advancements of technologies in the field of artificial intelligence and machine learning provide opportunities for researchers and scientists to step in this battlefield and convert the related data into a meaningful knowledge through computational-based models, for the task of containment the virus, diagnosis and providing treatment. In this study, we will provide recent developments and practical implementations of artificial intelligence modeling and machine learning algorithms proposed by researchers and practitioners during the pandemic period which suggest serious potential in compliant solutions for investigating diagnosis and decision making using computerized tomography (CT) scan imaging. We will review the modern algorithms in CT scan imaging modeling that may be used for detection, quantification, and tracking of Coronavirus and study how they can differentiate Coronavirus patients from those who do not have the disease.

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

scholarly journals Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

2021 ◽  
Vol 17 (08) ◽  
pp. 154
Author(s):  
Shakir Khan
Keyword(s):  
Machine Learning ◽  
Saudi Arabia ◽  
Prediction Models ◽  
Preventive Measures ◽  
Future Trend ◽  
Control Measures ◽  
World Health ◽  
Seir Model ◽  
The World ◽  
Health Organization

<p>The World Health Organization (WHO) reported the COVID-19 epidemic a global health emergency on January 30 and confirmed its transformation into a pandemic on March 11. China has been the hardest hit since the virus's outbreak, which may date back to late November. Saudi Arabia realized the danger of the Coronavirus in March 2020, took the initiative to take a set of pre-emptive decisions that preceded many countries of the world, and worked to harness all capabilities to confront the outbreak of the epidemic. Several researchers are currently using various mathematical and machine learning-based prediction models to estimate this pandemic's future trend. In this work, the SEIR model was applied to predict the epidemic situation in Saudi Arabia and evaluate the effectiveness of some epidemic control measures, and finally, providing some advice on preventive measures.</p>

scholarly journals Early Outbreak Analysis of COVID-19 Epidemic: China and Global Health Perspectives

2020 ◽  
Author(s):  
Samrat Kumar Dey ◽  
Md. Mahbubur Rahman ◽  
Umme Raihan Siddiqi ◽  
Arpita Howlader
Keyword(s):  
World Health Organization ◽  
Exploratory Data Analysis ◽  
Open Data ◽  
Data Sources ◽  
World Health ◽  
Johns Hopkins University ◽  
The World ◽  
Control And Prevention ◽  
Exploratory Data ◽  
Health Organization

Abstract Purpose: Globally, there is an obvious concern about the fact that the evolving 2019-nCoV coronavirus is a worldwide public health threat. The appearance in China at the end of 2019 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; previously provisionally labeled as 2019 novel coronavirus or 2019-nCoV) disease (COVID-19) caused a major global outbreak and right now is a major community health issue. As of 8 March 2020, World Health Organization (WHO) data showed that more than 105 500 confirmed cases were reported in over 100 countries/regions, with > 75% of cases being detected in China and >24% of cases detected globally. COVID-19 outbreak is evolving so rapidly; therefore, the available epidemiological data are essential to direct strategies for situational awareness and intervention. Methods: This article will present a visual exploratory data analysis (V-EDA) approach to collect and analyze COVID-19 data on epidemiological outbreaks. Various open data sources on the outbreak of COVID-19 provided by the World Health Organization (WHO), the Chinese Center for Disease Control and Prevention (CDC), the National Health Commission (NHC), Johns Hopkins University Interactive Dashboard and DXY.cn have been used in this research.Results: Therefore, an Exploratory Data Analysis (EDA) with visualizations has been designed and developed in order to understand the number of different cases reported (confirmed, death, and recovered) in different provinces of China and outside of China between 22 January 2020 to 4 March 2020. Various open data sources on the outbreak of COVID-19 provided by the World Health Organization (WHO), the Chinese Center for Disease Control and Prevention (CDC), the National Health Commission (NHC), Johns Hopkins University Interactive Dashboard and DXY.cn have been used in this research. Conclusion: In all, this is extremely important to promptly spread information to understand the risks of this pandemic and begin containment activities.

scholarly journals Correlation Between Temperature and COVID-19 (Suspected, Confirmed and Death) Cases based on Machine Learning Analysis

2020 ◽  
Vol 14 (suppl 1) ◽  
pp. 1017-1024 ◽  
Author(s):  
Mohammad Khubeb Siddiqui ◽  
Ruben Morales-Menendez ◽  
Pradeep Kumar Gupta ◽  
Hafiz M.N. Iqbal ◽  
Fida Hussain ◽  
...  
Keyword(s):  
Machine Learning ◽  
World Health ◽  
Effect Of Temperature ◽  
Machine Learning Method ◽  
Contributing Factors ◽  
Data Set ◽  
The World ◽  
Health Organization ◽  
Death Cases ◽  
Learning Analysis

Currently, the whole world is struggling with the biggest health problem COVID-19 name coined by the World Health Organization (WHO). This was raised from China in December 2019. This pandemic is going to change the world. Due to its communicable nature, it is contagious to both medically and economically. Though different contributing factors are not known yet. Herein, an effort has been made to find the correlation between temperature and different cases situation (suspected, confirmed, and death cases). For a said purpose, k-means clustering-based machine learning method has been employed on the data set from different regions of China, which has been obtained from the WHO. The novelty of this work is that we have included the temperature field in the original WHO data set and further explore the trends. The trends show the effect of temperature on each region in three different perspectives of COVID-19 – suspected, confirmed and death.

scholarly journals Ultra-Sensor Based Social Distancing Device

2021 ◽  
pp. 237-241
Author(s):  
K. Harshita ◽  
R. Moni Pravallika ◽  
T. Lakshmi Prasanna ◽  
Sk. Nazma ◽  
S. Parvathi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
World Health Organization ◽  
Social Distance ◽  
Outdoor Environment ◽  
World Health ◽  
Localization Method ◽  
Social Distancing ◽  
The Social ◽  
The World ◽  
Health Organization

According to the world health organization, social distancing will be proven to be the only solution to fight with COVID-19. In this, an innovative localization method was proposing to track humans ‘position in an outdoor environment based on sensors is proposed with the help of artificial intelligence, this device is handy to maintain a social distancing. Duringcovid-19pandemicsituation, there is a need of maintaining social distance. If any person is approaching us, getting indication to maintain social distance is the need of the hour. Offices, public transports, grocery shops where the social distancing is mandatory. Since we can be cautious in front sideways to maintain the distance sensors are used in this model to alert the person to maintain social distance.

Predicting the Linguistic Accessibility of Chinese Health Translations: Using Machine Learning Algorithms (Preprint)

2021 ◽  
Author(s):  
Meng Ji ◽  
Pierrette Bouillon
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Health Resources ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Learning Models ◽  
The World ◽  
Health Organization ◽  
C5.0 Decision Tree ◽  
Machine Learning Models

BACKGROUND Linguistic accessibility has important impact on the reception and utilization of translated health resources among multicultural and multilingual populations. Linguistic understandability of health translation has been under-studied. OBJECTIVE Our study aimed to develop novel machine learning models for the study of the linguistic accessibility of health translations comparing Chinese translations of the World Health Organization health materials with original Chinese health resources developed by the Chinese health authorities. METHODS Using natural language processing tools for the assessment of the readability of Chinese materials, we explored and compared the readability of Chinese health translations from the World Health Organization with original Chinese materials from China Centre for Disease Control and Prevention. RESULTS Pairwise adjusted t test showed that three new machine learning models achieved statistically significant improvement over the baseline logistic regression in terms of AUC: C5.0 decision tree (p=0.000, 95% CI: -0.249, -0.152), random forest (p=0.000, 95% CI: 0.139, 0.239) and XGBoost Tree (p=0.000, 95% CI: 0.099, 0.193). There was however no significant difference between C5.0 decision tree and random forest (p=0.513). Extreme gradient boost tree was the best model having achieved statistically significant improvement over the C5.0 model (p=0.003) and the Random Forest model (p=0.006) at the adjusted Bonferroni p value at 0.008. CONCLUSIONS The development of machine learning algorithms significantly improved the accuracy and reliability of current approaches to the evaluation of the linguistic accessibility of Chinese health information, especially Chinese health translations in relation to original health resources. Although the new algorithms developed were based on Chinese health resources, they can be adapted for other languages to advance current research in accessible health translation, communication, and promotion.

scholarly journals Towards an Ontology Proposal Model in Data Lake for Real-time COVID-19 Cases Prevention

2020 ◽  
Vol 16 (09) ◽  
pp. 123
Author(s):  
Jabrane Kachaoui ◽  
Jihane Larioui ◽  
Abdessamad Belangour
Keyword(s):  
Big Data ◽  
Real Time ◽  
Genetic Mutation ◽  
World Health ◽  
Semantic Approach ◽  
Significant Information ◽  
Multiple Data ◽  
Novel Approach ◽  
The World ◽  
Health Organization

Globally, the coronavirus epidemic has now hit lives of millions and thousands of people around the world. The growing threat of this virus continues rising as new cases appear every day. Yet, affected countries by coronavirus are currently taking important measures to remedy it by using artificial intelligence (AI) and Big Data technologies. According to the World Health Organization (WHO), AI and Big Data have performed an important role in China's response to COVID-19, the genetic mutation name for coronavirus. Predicting an epidemic emergence, from the corona virus appearance to a person's predisposition to develop it, is fundamental to combating it. In this battle, Big Data is on the front line. However, Big Data cannot provide all of the expected insights and derive value from manipulated data. This is why we propose a semantic approach to facilitate the use of these data. In this paper, we present a novel approach that combines between the Semantic Web Services (SWS) and the Big Data characteristics in order to extract a significant information from multiple Data sources that can be exploitable for generating real-time statistics and reports.

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