scholarly journals Bring on the Machines: Could Machine Learning Improve the Quality of Patient Education Materials? A Systematic Search and Rapid Review

2018 ◽  
pp. 1-16 ◽  
Author(s):  
Catherine H. Saunders ◽  
Curtis L. Petersen ◽  
Marie-Anne Durand ◽  
Pamela J. Bagley ◽  
Glyn Elwyn
Keyword(s):  
Machine Learning ◽  
Patient Education ◽  
English Language ◽  
Development Stage ◽  
Learning Technologies ◽  
Rapid Review ◽  
Learning Technology ◽  
Patient Centeredness ◽  
Education Material

PurposeClear and trustworthy information is essential for people who are ill. People with cancer, in particular, are targeted with vast quantities of patient education material, but of variable quality. Machine learning technologies are popular across industries for automated tasks, like analyzing language and spotting readability issues. With the experience of patients with cancer in mind, we reviewed whether anyone has proposed, modeled, or applied machine learning technologies for the assessment of patient education materials and explored the utility of this application.MethodsWe systematically searched the literature to identify English-language articles published in peer-reviewed journals or as conference abstracts that proposed, used, or modeled the use of machine learning technology to assess patient education materials. Specifically, we searched MEDLINE, Web of Science, CINAHL, and Compendex. Two reviewers assessed study eligibility and performed study screening.ResultsWe identified 1,570 publications in our search after duplicate removal. After screening, we included five projects (detailed in nine articles) that proposed, modeled, or used machine learning technology to assess the quality of patient education materials. We evaluated the utility of each application across four domains: multidimensionality (2 of 5 applications), patient centeredness (1 of 5 applications), customizability (0 of 5 applications), and development stage (theoretical, 1 of 5 applications; in development, 3 of 5 applications; complete and available, 1 of 5 applications). Combining points across each domain, the mean utlity score across included projects was 1.8 of 5 possible points.ConclusionGiven its potential, machine learning has not yet been leveraged substantially in the assessment of patient education materials. We propose machine learning systems that can dynamically identify problematic language and content by assessing the quality of patient education materials across a range of flexible, customizable criteria. Assessment may help patients and families decide which materials to use and encourage developers to improve materials overall.

Quality, understandability and actionability of online patient education material about hypertension

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohd Ramadan Ab Hamid ◽  
Mastura Mohd Isamudin ◽  
Siti Sabariah Buhari ◽  
Emmy Hainida Khairul Ikram
Keyword(s):  
Patient Education ◽  
English Language ◽  
Assessment Tool ◽  
Online Information ◽  
Point Scale ◽  
Cross Sectional ◽  
Web Based ◽  
Content Type ◽  
Education Material

Purpose The purpose of this paper is to assess the value of websites accessible to patients looking for Web-based information regarding hypertension management. Design/methodology/approach A cross-sectional research was carried out by finding out Malay and English language websites about hypertension. For this purpose, the keywords “hypertension and treatment” were entered on the Yahoo, Google, Ask.com, Bing and DuckDuckGo search engines, and the first five pages of the results obtained were inspected. The DISCERN tool was deployed for evaluating the quality of information. The actionability and understandability were assessed through the Patient Education Materials Assessment Tool (PEMAT). Eight assessors were asked to assess and grade the involved websites. Findings Of the 216 websites, eight (4.0%) conformed to the inclusion norms. All websites were classified into private, 4 (50%); government, 2 (25%) and personal, 2 (25%). The general rating of the eight websites was good (mean 51.6 ± 8.2 on a 75-point scale); however, half of the websites were rated as fair (mean 45.3 ± 3.1 on a 75-point scale). All websites conformed to the standard score of ≥70% for understandability (mean 76.1 ± 11.4), but none for actionability (mean 52.8 ± 13.9). Analysis of variance indicated there was no statistical difference with regards to quality (p = 0.525), understandability (p = 0.484) and actionability (p = 0.188) among the three website sets. Originality/value Considering the surplus of websites dedicated to information on hypertension, an independent assessment of the quality of these websites will be advantageous. Patients should be rendered high understandability, quality and actionability to evade deceptive online information.

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

scholarly journals Using research to inform learning technology practice and policy: a qualitative analysis of student perspectives

2014 ◽  
Vol 30 (1) ◽  
Author(s):  
Carol Russell ◽  
Janne Malfroy ◽  
Maree Gosper ◽  
Jo McKenzie
Keyword(s):  
Student Learning ◽  
Learning Technologies ◽  
Student Experiences ◽  
Education Student ◽  
Learning Technology ◽  
Strategic Choices ◽  
Institutional Goals ◽  
Institutional Learning ◽  
New Strategies

<p>As learning technologies are now integral to most higher education student learning experiences, universities need to make strategic choices about what technologies to adopt and how to best support and develop the use of these technologies, particularly in a climate of limited resources. Information from students is therefore a valuable contribution when determining institutional goals, building infrastructure and improving the quality of student learning. This paper draws on a survey of student experiences and expectations of technology across three Australian universities. Analysis of text responses from 7,000 students provides insight into ways that institutional learning technologies and academic-led technologies are influencing the student experience. This paper also discusses how the three universities have used this information to develop strategic initiatives, and identifies a need for new strategies to support academic-led use of the available tools.</p><p> </p>

scholarly journals Has e-Learning Delivered on its Promises? Expert Opinion on the Impact of e-Learning in Higher Education

1969 ◽  
Vol 38 (1) ◽  
pp. 45-65 ◽  
Author(s):  
Heather Kanuka ◽  
Jennifer Kelland
Keyword(s):  
Higher Education ◽  
Learning Experience ◽  
Learning Technologies ◽  
Learning Technology ◽  
Canadian Higher Education ◽  
Education Leaders ◽  
E Learning ◽  
Higher Education Leaders ◽  
The Impact

The purpose of this study was to determine the extent of agreement among experts on the impact of e-learning technology in Canadian higher education learning experiences. Fourteen participants who are experts in e-learning in higher education agreed there are contentions about e-learning technologies in the following areas: (1) a platform for ideal speech; (2) greater opportunities for interactions; (3) the extent to which communities of learners can be created; (4) provision of a new kind of learning environment; (5) a platform for discussions; (6) demand for e-learning by students; (7) the degree to which the environment is equal and equitable; and (8) the quality of the learning experience. The findings of this study indicate that the value of e-learning requires further research before higher education leaders and teacher-practitioners are willing to incorporate them in teaching practices and policy documents.  

A Blood Bank Management System-Based Internet of Things and Machine Learning Technologies

2020 ◽  
pp. 184-222
Author(s):  
Ahmed Mousa ◽  
Ahmed El-Sayed ◽  
Ali Khalifa ◽  
Marwa El-Nashar ◽  
Yousra Mancy Mancy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Undergraduate Students ◽  
Management System ◽  
Main Idea ◽  
Learning Technologies ◽  
Bank Management ◽  
Academic Researchers ◽  
Blood Banks

Nearly all of the Egyptian hospitals are currently suffering from shortage in rare blood types (e.g., -AB, -B, +AB), which are needed to perform vital surgeries. This leads them (hospitals or doctors) to ask patients' relatives to donate the amount of the required blood. The alternative is that they are forced to pay for the blood if the required type and amount is already available in these hospitals or the blood banks. The main idea of this work is solving problems related to the blood banks from collecting blood from donators to distributing blood bags for interested hospitals. This system is developed in order to enhance the management, performance, and the quality of services for the management of blood banks, which will be positively reflected on many patients in hospitals. This chapter targets undergraduate students, academic researchers, development engineers, and course designers and instructors.

scholarly journals Review of Machine Learning Technologies and Neural Networks in Drug Synergy Combination pharmacological research

2020 ◽  
Vol 6 (3) ◽  
pp. 27-32
Author(s):  
Artur S. Ter-Levonian ◽  
Konstantin A. Koshechkin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Technologies ◽  
Learning Technology ◽  
Preclinical Research ◽  
Processing Technologies ◽  
Drug Synergy ◽  
Positive Effects ◽  
The Past ◽  
The Right

Introduction: Nowadays an increase in the amount of information creates the need to replace and update data processing technologies. One of the tasks of clinical pharmacology is to create the right combination of drugs for the treatment of a particular disease. It takes months and even years to create a treatment regimen. Using machine learning (in silico) allows predicting how to get the right combination of drugs and skip the experimental steps in a study that take a lot of time and financial expenses. Gradual preparation is needed for the Deep Learning of Drug Synergy, starting from creating a base of drugs, their characteristics and ways of interacting. Aim: Our review aims to draw attention to the prospect of the introduction of Deep Learning technology to predict possible combinations of drugs for the treatment of various diseases. Materials and methods: Literary review of articles based on the PUBMED project and related bibliographic resources over the past 5 years (2015–2019). Results and discussion: In the analyzed articles, Machine or Deep Learning completed the assigned tasks. It was able to determine the most appropriate combinations for the treatment of certain diseases, select the necessary regimen and doses. In addition, using this technology, new combinations have been identified that may be further involved in preclinical studies. Conclusions: From the analysis of the articles, we obtained evidence of the positive effects of Deep Learning to select “key” combinations for further stages of preclinical research.

scholarly journals REFINEMENT OF CROPLAND DATA LAYER USING MACHINE LEARNING

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 161-164 ◽  
Author(s):  
C. Zhang ◽  
Z. Yang ◽  
L. Di ◽  
L. Lin ◽  
P. Hao
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Learning Technology ◽  
Landsat Images ◽  
Data Layer ◽  
Crop Types ◽  
Crop Area ◽  
Very High ◽  
Cropland Data Layer

Abstract. As the most widely used crop-specific land use data, the Cropland Data Layer (CDL) product covers the entire Contiguous United States (CONUS) at 30-meter spatial resolution with very high accuracy up to 95% for major crop types (i.e., Corn, Soybean) in major crop area. However, the quality of early-year CDL products were not as good as the recent ones. There are many erroneous pixels in the early-year CDL product due to the cloud cover of the original Landsat images, which affect many follow-on researches and applications. To address this issue, we explore the feasibility of using machine learning technology to refine and correct misclassified pixels in the historical CDLs in this study. An end-to-end deep learning-based framework for restoration of misclassified pixels in CDL image is developed and tested. By feeding the CDL time series into the artificial neural network, a crop sequence model is trained and the misclassified pixels in an original CDL map can be restored. In the experiment with the 2005 CDL data of the State of Illinois, the misclassified pixels over Agricultural Statistics Districts (ASD) #1760 were corrected with a reasonable accuracy (> 85%). The findings suggest that the proposed method provides a low-cost and reliable way to refine the historical CDL data, which can be potentially scaled up to the entire CONUS.

Readability and Quality of Online Patient Education Material on Websites of Breast Imaging Centers

2020 ◽  
Vol 17 (10) ◽  
pp. 1245-1251
Author(s):  
Sadia Choudhery ◽  
Yin Xi ◽  
Heng Chen ◽  
Nader Aboul-Fettouh ◽  
YPaul Goldenmerry ◽  
...  
Keyword(s):  
Patient Education ◽  
Breast Imaging ◽  
Patient Education Material ◽  
Education Material ◽  
Online Patient Education

scholarly journals Application of machine learning technologies for Drone Network in logistics and port activities of Russia

2021 ◽  
pp. 149-157
Author(s):  
О.М. Михов ◽  
Н.В. Шаталова ◽  
О.В. Бородина ◽  
Ю.И. Васильев
Keyword(s):  
Machine Learning ◽  
Learning Technologies ◽  
Scientific Article ◽  
Learning Technology ◽  
Foreign Companies ◽  
International Trends ◽  
Logistics Enterprises ◽  
The Cost ◽  
Network Technologies ◽  
Transport Logistics

Cтатья посвящена проведению исследовательского анализа особенностей практического использования беспилотных дронов и квадрокоптеров. Актуальность исследования обусловлена тем, что технологии Drone Network позволяют решить основную проблему логистики – провести оптимизацию финансовых расходов, путем сокращения затрат на реализацию цепочек поставок. Это помощь интеграции логистики, дронов и технологии машинного обучения. В рамках статьи рассмотрены теоретические аспекты понятия технологии «Drone Network». Проанализирован зарубежный опыт и международные тенденции в использовании беспилотных дронов при формировании цепочки поставок логистики морских предприятий. Рассмотрены ключевые преимущества, которые предоставляют данные технологии в совершенствовании транспортной логистики компаний. Проанализированы перспективы развития технологий Drone Network на территории Российской Федерации. Рассмотрены основные проблемы, препятствующие их практическому применению российскими компаниями. Проанализированы недостатки, с которыми сталкиваются организации в рамках использования технологий беспилотных дронов в логистике. Описаны перспективы развития технологий Drone Network в международном и российском рынке. Проанализированы перспективы применения беспилотных дронов, управляемых технологиями машинного обучения, в рамках развития портовой деятельности, внутрипортовой логистики и для поиска бедствующих кораблей. The scientific article is devoted to the research analysis of the features of the practical use of unmanned drones and frame copters in the framework of the transport logistics of goods and orders by foreign companies. The relevance of the study is due to the fact that Drone Network technologies allow solving the main problem of logistics - to optimize financial costs by reducing the cost of implementing supply chains. Perhaps this is helping the integration of logistics, drones and machine learning technology. The article discusses the theoretical aspects of the concept of the "Drone Network" technology. Analyzed foreign experience and international trends in the use of unmanned drones in the formation of the supply chain of logistics enterprises. The key advantages that these technologies provide in improving the transport logistics of companies are considered. The prospects for the development of Drone Network technologies on the territory of the Russian Federation are analyzed. The main problems that hinder their practical application by Russian companies are considered. The paper analyzes the shortcomings faced by organizations in the use of unmanned drone technologies in logistics. The prospects for the development of Drone Network technologies in the international and Russian markets are described. The prospects for the use of unmanned drones controlled by machine learning technologies in the development of port activities, intra-port logistics and for the search for distressed ships are analyzed.

scholarly journals EFFICIENT USE INTERACTIVE LEARNING TECHNOLOGIES IN HIGHER EDUCATIONAL INSTITUTIONS

2020 ◽  
Vol 66 (2) ◽  
pp. 137-142
Author(s):  
K.T. Zhansugurova ◽  
Keyword(s):  
Interactive Learning ◽  
Learning Technologies ◽  
Educational Process ◽  
Learning Technology ◽  
Interactive Technologies ◽  
Technology Learning ◽  
Education Market ◽  
The One ◽  
Main Ideas

Higher education in our country at present is characterized, on the one hand, continue to improve, rethinking and revision of conceptual frameworks, and with another - increased competition in the education market, increased requirements to the innovative potential of teachers, increased requirements to the quality of the educational process. Today, virtually exhausted reserves of growth of efficiency and quality of training of students, based on the use of word-book. The article is based on the relevance of interactive learning technology. Learning methods using interactive learning technologies are considered. The main ideas of interactive technologies are highlighted.

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