scholarly journals Artificial Intelligence (AI) in Evidence-Based Approaches to Effectively Respond to Public Health Emergencies

2021 ◽  
Author(s):  
Lap Yan Wong ◽  
Chun Kit Yip ◽  
Dao Shen Tan ◽  
Wai Lim Ling
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Early Warning ◽  
Evidence Based ◽  
Public Health Emergencies ◽  
Wide Range ◽  
Statistical Approaches ◽  
Public Health Strategies

Artificial intelligence (AI) techniques have been commonly used to track, predict early warning, forecast trends, and model and measure public health responses. Statistics have traditionally been used to track public health crises. AI-enabled methods, such as machine learning and deep learning–based models, have exploded in popularity recently, complementing statistical approaches. A wide range of medical fields have used various well-developed deep learning algorithms. Surveillance of public health emergencies is one region that has gained greatly from AI advancements in recent years. One of the examples of effectively reacting to public health emergencies is the need for developing AI evidence-based approaches to public health strategies for the scientific community’s response to the COVID-19 pandemic.

scholarly journals ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

2021 ◽  
Vol 65 (2) ◽  
Author(s):  
Oleksandr Dudin ◽  
◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Personalized Medicine ◽  
Digital Pathology ◽  
Automated Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

The organization of effective multi-user operation of hybrid computing systems

2019 ◽  
Author(s):  
С.И. Смагин ◽  
А.А. Сорокин ◽  
С.И. Мальковский ◽  
С.П. Королёв ◽  
О.А. Лукьянова ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Parallel Programming ◽  
Computer Applications ◽  
Computing Systems ◽  
Scheduling System ◽  
Hybrid Computing ◽  
Wide Range ◽  
Virtualization Technology

Исследуются вопросы организации многопользовательской работы гибридных вычислительных систем. На примере кластера Центра коллективного пользования Центр данных ДВО РАН, построенного на архитектуре OpenPOWER, рассмотрены особенности функционирования систем подобного класса и предложены решения для организации их работы. С использованием механизма виртуальных узлов проведена адаптация системы диспетчеризации заданий PBS Professional, позволяющая организовать эффективное распределение аппаратных ресурсов кластера между пользовательскими задачами. Реализованное программное окружение кластера с системой комплексного планирования заданий рассчитано на работу с широким перечнем компьютерных приложений, включая программы, построенные с использованием различных технологий параллельного программирования. Для эффективного исполнения в данной среде решений на основе машинного обучения, глубокого обучения и искусственного интеллекта применены технологии виртуализации. С использованием возможностей среды контейнеризации Singularity сформирован специализированный стек программного обеспечения и реализован особый режим его работы в формате единой вычислительной цифровой платформы. Purpose. Improving the technology of machine learning, deep learning and artificial intelligence plays an important role in acquiring new knowledge, technological modernization and the digital economy development.An important factor of the development in these areas is the availability of an appropriate highperformance computing infrastructure capable of providing the processing of large amounts of data. The creation of coprocessorbased hybrid computing systems, as well as new parallel programming technologies and application development tools allows partial solving this problem. However, many issues of organizing the effective multiuser operation of this class of systems require a separate study. The current paper addresses research in this area. Methodology. Using the OpenPOWER architecturebased cluster in the Shared Services Center The Data Center of the Far Eastern Branch of the Russian Academy of Sciences, the features of the functioning of hybrid computing systems are considered and solutions are proposed for organizing their work in a multiuser mode. Based on the virtual nodes concept, an adaptation of the PBS Professional job scheduling system was carried out, which provides an efficient allocation of cluster hardware resources among user tasks. Application virtualization technology was used for effective execution of machine learning and deep learning problems. Findings. The implemented cluster software environment with the integrated task scheduling system is designed to work with a wide range of computer applications, including programs built using parallel programming technologies. The virtualization technologies were used in this environment for effective execution of the software, based on machine learning, deep learning and artificial intelligence. Having the capabilities of the container Singularity, a specialized software stack and its operation mode was implemented for execution machine learning, deep learning and artificial intelligence tasks on a unified computing digital platform. Originality. The features of hybrid computing platforms functioning are considered, and the approach for their effective multiuser work mode is proposed. An effective resource manage model is developed, based on the virtualization technology usage.

Applications of Artificial Intelligence and Machine Learning in Disasters and Public Health Emergencies

2021 ◽  
pp. 1-8
Author(s):  
Sally Lu ◽  
Gordon A. Christie ◽  
Thanh T. Nguyen ◽  
Jeffrey D. Freeman ◽  
Edbert B. Hsu
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Full Text ◽  
Public Health Emergency ◽  
Machine Learning Algorithms ◽  
Inclusion Criteria ◽  
Public Health Emergencies ◽  
Full Text Review ◽  
Emergency Scenarios

Abstract Indexed literature (from 2015 to 2020) on artificial intelligence (AI) technologies and machine learning algorithms (ML) pertaining to disasters and public health emergencies were reviewed. Search strategies were developed and conducted for PubMed and Compendex. Articles that met inclusion criteria were filtered iteratively by title followed by abstract review and full text review. Articles were organized to identify novel approaches and breadth of potential AI applications. A total of 1217 articles were initially retrieved by the search. Upon relevant title review, 1003 articles remained. Following abstract screening, 667 articles remained. Full text review for relevance yielded 202 articles. Articles that met inclusion criteria totaled 56 articles. Those identifying specific roles of AI and ML (17 articles) were grouped by topics highlighting utility of AI and ML in disaster and public health emergency contexts. Development and use of AI and ML have increased dramatically over the past few years. This review discusses and highlights potential contextual applications and limitations of AI and ML in disaster and public health emergency scenarios.

scholarly journals Early Warning Method for Public Health Emergency Under Artificial Neural Network in the Context of Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Shuang Zheng ◽  
Xiaomei Hu
Keyword(s):  
Public Health ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Early Warning ◽  
Structural Equation ◽  
Equation Model ◽  
Hybrid Network ◽  
Public Health Emergencies ◽  
Artificial Neural

The purpose is to minimize the substantial losses caused by public health emergencies to people’s health and daily life and the national economy. The tuberculosis data from June 2017 to 2019 in a city are collected. The Structural Equation Model (SEM) is constructed to determine the relationship between hidden and explicit variables by determining the relevant indicators and parameter estimation. The prediction model based on Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) is constructed. The method’s effectiveness is verified by comparing the prediction model’s loss value and accuracy in training and testing. Meanwhile, 50 pieces of actual cases are tested, and the warning level is determined according to the T-value. The results show that comparing and analyzing ANN, CNN, and the hybrid network of ANN and CNN, the hybrid network’s accuracy (95.1%) is higher than the other two algorithms, 89.1 and 90.1%. Also, the hybrid network has sound prediction effects and accuracy when predicting actual cases. Therefore, the early warning method based on ANN in deep learning has better performance in public health emergencies’ early warning, which is significant for improving early warning capabilities.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals 18.H. Workshop: Bringing evidence during public health emergencies: addressing the challenge through HTA

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Decision Making ◽  
Emergency Preparedness ◽  
Public Health Emergency ◽  
Decision Makers ◽  
Evidence Based ◽  
Public Health Emergencies ◽  
Response Planning ◽  
Emergency Preparedness And Response ◽  
Public Health Emergency Preparedness

Abstract Evidence-based decision-making is central to public health. Implementing evidence-informed actions is most challenging during a public health emergency as in an epidemic, when time is limited, scientific uncertainties and political pressures tend to be high, and irrefutable evidence may be lacking. The process of including evidence in public health decision-making and for evidence-informed policy, in preparation, and during public health emergencies, is not systematic and is complicated by many barriers as the absences of shared tools and approaches for evidence-based preparedness and response planning. Many of today's public health crises are also cross-border, and countries need to collaborate in a systematic and standardized way in order to enhance interoperability and to implement coordinated evidence-based response plans. To strengthen the impact of scientific evidence on decision-making for public health emergency preparedness and response, it is necessary to better define mechanisms through which interdisciplinary evidence feeds into decision-making processes during public health emergencies and the context in which these mechanisms operate. As a multidisciplinary, standardized and evidence-based decision-making tool, Health Technology Assessment (HTA) represents and approach that can inform public health emergency preparedness and response planning processes; it can also provide meaningful insights on existing preparedness structures, working as bridge between scientists and decision-makers, easing knowledge transition and translation to ensure that evidence is effectively integrated into decision-making contexts. HTA can address the link between scientific evidence and decision-making in public health emergencies, and overcome the key challenges faced by public health experts when advising decision makers, including strengthening and accelerating knowledge transfer through rapid HTA, improving networking between actors and disciplines. It may allow a 360° perspective, providing a comprehensive view to decision-making in preparation and during public health emergencies. The objective of the workshop is to explore and present how HTA can be used as a shared and systematic evidence-based tool for Public Health Emergency Preparedness and Response, in order to enable stakeholders and decision makers taking actions based on the best available evidence through a process which is systematic and transparent. Key messages There are many barriers and no shared mechanisms to bring evidence in decision-making during public health emergencies. HTA can represent the tool to bring evidence-informed actions in public health emergency preparedness and response.

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