Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines

Washing hands is one of the most important ways to prevent infectious diseases, including COVID-19. The World Health Organization (WHO) has published hand-washing guidelines. This paper presents a large real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Pauls Stradins Clinical University Hospital. There are 3185 hand-washing episodes in total, each of which is annotated by up to seven different persons. The annotations classify the washing movements according to the WHO guidelines by marking each frame in each video with a certain movement code. The intention of this “in-the-wild” dataset is two-fold: to serve as a basis for training machine-learning classifiers for automated hand-washing movement recognition and quality control, and to allow to investigation of the real-world quality of washing performed by working medical staff. We demonstrate how the data can be used to train a machine-learning classifier that achieves classification accuracy of 0.7511 on a test dataset.

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Coronavirus Disease 2019 Occupational Considerations for Surgeons: A Review Article

Disease and Diagnosis ◽

10.34172/ddj.2021.07 ◽

2021 ◽

Vol 10 (1) ◽

pp. 36-41

Author(s):

Seyed Hesamaddin Banihashemi ◽

Ahmadreza Karimi ◽

Hasti Nikourazm ◽

Behnaz Bahmanyar ◽

Dariush Hooshyar

Keyword(s):

Severe Acute Respiratory Syndrome ◽

World Health Organization ◽

Medical Staff ◽

Review Article ◽

World Health ◽

Published Data ◽

Rapid Spread ◽

The World ◽

Health Organization ◽

And Control

The severe acute respiratory syndrome coronavirus 2 virus and its associated disease, called coronavirus disease 2019 (COVID-19), first appeared in Wuhan, China in December 2019 and quickly spread around the world. Coronavirus was officially named COVID-19 by the World Health Organization and was recognized as a pandemic due to its rapid spread worldwide. Based on the published data, it is hoped to provide a source for later studies and to help prevent and control the contagious COVID-19 and its characteristics, and considerations that surgeons and medical staff must observe during the epidemic.

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Web-Based Machine Learning Application for Heart Disease Prediction

10.4018/978-1-7998-7709-7.ch022 ◽

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.

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Patient Safety Concerns among Emergency Medical Staff and Patients

International Journal of Privacy and Health Information Management ◽

10.4018/ijphim.2013010103 ◽

2013 ◽

Vol 1 (1) ◽

pp. 29-52

Author(s):

Pi-Fang Hsu ◽

Wen-Chun Tsai ◽

Chia-Wen Tsai

Keyword(s):

Patient Safety ◽

Emergency Departments ◽

Medical Staff ◽

World Health ◽

The European Union ◽

Department Of Health ◽

Perceptions And Attitudes ◽

The World ◽

Health Organization

Recently, much of the world, including the World Health Organization, the European Union and many North American countries, have emphasized patient safety. Around the same time, Taiwan’s Department of Health (DOH) devoted a significant amount of resources to better the quality of medical treatment for their patients. This study explores perceptions of and attitudes towards patient safety among medical staff and patients in emergency departments. Analysis results indicate that medical staff and patients significantly differ in perceptions and attitudes. Results of this study provide a valuable reference for governmental authorities and hospital managers in formulating policies aimed at clarifying perceptions and attitudes regarding patient safety among medical staff and patients in emergency departments.

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11th Geneva Conference on Person Centered Medicine: Summary Report

The International Journal of Person Centered Medicine ◽

10.5750/ijpcm.v8i1.730 ◽

2019 ◽

Author(s):

C Ruth Wilson ◽

Juan E. Mezzich

Keyword(s):

Medical Association ◽

World Health Organization ◽

University Hospital ◽

World Health ◽

Annual Conference ◽

World Medical Association ◽

Family Doctors ◽

The World ◽

Health Organization ◽

World Organization

For the 11th time, the International College for Person-Centered Medicine (ICPCM) held its annual conference on Person-Centered Medicine in Geneva, Switzerland. As in previous years, the conference was supported by the World Health Organization, the World Medical Association, the World Organization of Family Doctors, the International Council of Nurses, the International Alliance of Patients’ Organizations and 30 other global health professional and academic institutions. The organizing committee was composed of the ICPCM Board members, with Ruth Wilson as program director. Material support was provided by the World Medical Association, the World Health Organization, the Geneva University Hospital, and the Paul Tournier Association.

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Fighting the SARS CoV-2 (COVID-19) Pandemic with Soap

10.20944/preprints202005.0060.v1 ◽

2020 ◽

Author(s):

Narendra Kumar Chaudhary ◽

Nabina Chaudhary ◽

Manis Dahal ◽

Biswash Guragain ◽

Sumie Rai ◽

...

Keyword(s):

General Public ◽

Preventive Measures ◽

Chemical Properties ◽

Cost Effective ◽

Hand Washing ◽

World Health ◽

Potential Effectiveness ◽

The World ◽

Health Organization ◽

Unique Chemical

Today, the entire globe is struggling to deal with the greatest pandemic of the century, COVID-19. With no clinically approved treatments available, we are left with no options other than following the preventive measures issued by the World Health Organization (WHO). Among many others, hand washing with soap and water has been emphasized the most because it is cost-effective and easily accessible to the general public. Various studies have reported that soaps offer unique chemical properties that can disinfect the virus as a whole. However, there is still ambiguity in the general public about whether soaps can really shield us from this highly contagious disease. In an attempt to help eliminate the ambiguity, we analyzed the mechanisms underlying the efficacy of soap and its prospect for preventing the spread of COVID-19. In this paper, we have provided an overview of the history and characteristics of SARS-CoV-2 (COVID-19), the detailed mechanisms of the deactivation of viruses by soaps, and the potential effectiveness of soap in eliminating coronaviruses including SARS-CoV-2.

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

10.1101/472555 ◽

2018 ◽

Cited By ~ 2

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.

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

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i08.20099 ◽

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>

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Cybersecurity and Countermeasures at the Time of Pandemic

Journal of Advanced Transportation ◽

10.1155/2021/6627264 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Rabie A. Ramadan ◽

Bassam W. Aboshosha ◽

Jalawi Sulaiman Alshudukhi ◽

Abdullah J. Alzahrani ◽

Ayman El-Sayed ◽

...

Keyword(s):

World Health Organization ◽

Hand Washing ◽

The Other ◽

World Health ◽

Research Articles ◽

Remote Work ◽

Related Research ◽

News Sources ◽

The World ◽

Health Organization

With the emergence of one of this century’s deadliest pandemics, coronavirus disease (COVID-19) has an enormous effect globally with a quick spread worldwide. This made the World Health Organization announce it as a pandemic. COVID-19 has pushed countries to follow new behaviors such as social distancing, hand washing, and remote work and to shut down organizations, businesses, and airports. At the same time, white hats are doing their best to accommodate the pandemic. However, while white hats are protecting people, black hats are taking advantage of the situation, which creates a cybersecurity pandemic on the other hand. This paper discusses the cybersecurity issues at this period due to finding information or finding another related research that had not been discussed before. This paper presents the cybersecurity attacks during the COVID-19 epidemic time. A lot of information has been collected from the World Health Organization (WHO), trusted organizations, news sources, official governmental reports, and available research articles. This paper then classifies the cybersecurity attacks and threats at the period of COVID-19 and provides recommendations and countermeasures for each type. This paper surveys the cybersecurity attacks and their countermeasures and reports the ongoing cybersecurity attacks and threats at this period of time. Moreover, it is also a step towards analyzing the efficiency of the country’s infrastructure as well as hackers and criminals’ social behavior at the time of the pandemic.

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

Journal of Pure and Applied Microbiology ◽

10.22207/jpam.14.spl1.40 ◽

2020 ◽

Vol 14 (suppl 1) ◽

pp. 1017-1024 ◽

Cited By ~ 13

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.

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