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Applications of Machine Learning, Big Data and Internet of Things in Health Management

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 19299

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Special Issue Editors

Dr. Ignacio Rodríguez-Rodríguez

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Chief Guest Editor

Departamento de Ingeniería de Comunicaciones, Universidad de Málaga, 29071 Málaga, Spain;
Interests: machine learning, time series, diabetes, diosensors; social issues
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. José-Victor Rodríguez

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Assistant Guest Editor

Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
Interests: radiowave propagation; acoustics; biosensors; machine learning; diabetes
Special Issues, Collections and Topics in MDPI journals

Dr. Andrés Ortiz García

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Assistant Guest Editor

Departamento de Ingeniería de Comunicaciones, Universidad de Málaga, 29071 Málaga, Spain
Interests: intelligence systems; neurosciences; signal processing with biomedical applications; high performance computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of groundbreaking technological concepts is changing the way we approach daily life. There is no work, social, or leisure environment that has not been affected by the influence of increasingly familiar concepts such as machine learning (ML), deep learning (DL), Internet of Things (IoT), cloud computing, and big data. The health field has been particularly affected, allowing health professionals to draw on these new ideas in not only generating proper diagnoses but also choosing, managing, and monitoring medical treatments over time. Furthermore, artificial intelligence (AI) together with the use of cloud-connected biosensors offers an enormous set of novel possibilities in the field of medical care.

In another vein, the use of ML algorithms and other related ideas is also helping to promote health. From a recreational point of view, these advances enable people to manage their physical condition and set new goals in healthcare, fostering motivation and promoting healthy habits.

On the other hand, the current global situation caused by the COVID-19 pandemic has forcefully underlined the necessity of concepts such as IoT and AI. For example, their use is crucial to controlling infections at the population level, monitoring biomedical signals in people at risk, and increasing access to online and remote medicine.

The focus of this Special Issue will be on a broad range of topics spanning the IoT, ML, biosensors, and data fusion with the aim of providing better care to people in terms of diagnosis and medical treatment as well as promoting health.

Potential topics include, but are not limited to:

IoT, ML, and AI-driven applications for healthcare;
Biosensors/wearable sensors and the remote monitoring of patients;
Artificial vision, big data analysis, and AI focused on diagnosis;
Pervasive mobile computing and wireless sensor networks: communication and applications in healthcare;
Data warehouse strategies and privacy applied to the mass storage of medical data from patients;
Human–computer interactions for telemedicine: cyber-physical-social systems and constructs;
IoT platforms for the management of chronic diseases;
Gamification and the health promotion of healthy habits, physical activities, and sports;
Applications of IoT and ML in the prevention and management of COVID-19;
Other emerging applications of intelligent IoT.

Dr. Ignacio Rodríguez-Rodríguez
Prof. Dr. Andrés Ortiz García
Dr. José-Victor Rodríguez

Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

healthcare
Internet of Things
machine learning
computational intelligence
mobile computing
social signal processing
wireless sensor networks
human–computer interaction
biosignals, image, and video processing
COVID-19 management

Published Papers (6 papers)

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Research

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27 pages, 4490 KiB

Open AccessArticle

Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques

by Saad Awadh Alanazi, Ayesha Khaliq, Fahad Ahmad, Nasser Alshammari, Iftikhar Hussain, Muhammad Azam Zia, Madallah Alruwaili, Alanazi Rayan, Ahmed Alsayat and Salman Afsar

Int. J. Environ. Res. Public Health 2022, 19(15), 9695; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19159695 - 06 Aug 2022

Cited by 9 | Viewed by 2584

Abstract

Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public’s mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public’s mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions. Full article

(This article belongs to the Special Issue Applications of Machine Learning, Big Data and Internet of Things in Health Management)

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10 pages, 1161 KiB

Open AccessArticle

The Prevalence of Teleophthalmology in the Piedmont Region of Italy: Current Situation and Future Perspectives

by Raffaele Nuzzi, Floriana Deraco and Simona Scalabrin

Int. J. Environ. Res. Public Health 2022, 19(14), 8608; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19148608 - 15 Jul 2022

Viewed by 1214

Abstract

Purpose: This cross-sectional study aims to evaluate the actual prevalence of telemedicine, with a particular attention to teleophthalmology programs, in the Piedmont region of Northern Italy and investigate the prospects of the discipline, comparing the situation with the rest of Italy. Information about the current teleophthalmological development is important to assess the state-of-the-art of innovation in medicine in different areas of the world so that there can be a healthy comparison and evaluation of progress and backlog. Methods: We sent questionnaires to every Local Healthcare Authority and gathered the answers in five distinct categories. Due to the COVID-19 pandemic, we collected information by telephone. We investigated five primary areas: what type of telemedicine services are currently in operation, how they are managed, the presence of any evaluation of patient satisfaction and cost-savings, and the prospects of future teleophthalmology programs to be implemented. Results: Only 2% of the total telemedicine programs are in the field of ophthalmology. The greater parts are in the field of cardiology (15%) and endocrinology (13%). Currently, only one program of teleophthalmology exists in the territory, and at least four more projects are waiting to be approved or funded. Surveys about patient satisfaction were not provided and there was no evidence of cost-saving. Conclusions: Teleophthalmology in Piedmont is slowly developing, although there is a lack of a unified network for storing and managing patients’ data. During the COVID-19 pandemic, telemedicine usage drastically increased, and there is a need to evaluate this trend to understand its place in the future of medicine. Full article

(This article belongs to the Special Issue Applications of Machine Learning, Big Data and Internet of Things in Health Management)

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28 pages, 2528 KiB

Open AccessArticle

Usability Evaluation and Classification of mHealth Applications for Type 2 Diabetes Mellitus Using MARS and ID3 Algorithm

by Kamaldeep Gupta, Sharmistha Roy, Ayman Altameem, Raghvendra Kumar, Abdul Khader Jilani Saudagar and Ramesh Chandra Poonia

Int. J. Environ. Res. Public Health 2022, 19(12), 6999; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19126999 - 08 Jun 2022

Cited by 3 | Viewed by 2187

Abstract

The rapid growth of mHealth applications for Type 2 Diabetes Mellitus (T2DM) patients’ self-management has motivated the evaluation of these applications from both the usability and user point of view. The objective of this study was to identify mHealth applications that focus on T2DM from the Android store and rate them from the usability perspective using the MARS tool. Additionally, a classification of these mHealth applications was conducted using the ID3 algorithm to identify the most preferred application. The usability of the applications was assessed by two experts using MARS. A total of 11 mHealth applications were identified from the initial search, which fulfilled our inclusion criteria. The usability of the applications was rated using the MARS scale, from 1 (inadequate) to 5 (excellent). The Functionality (3.23) and Aesthetics (3.22) attributes had the highest score, whereas Information (3.1) had the lowest score. Among the 11 applications, “mySugr” had the highest average MARS score for both Application Quality (4.1/5) as well as Application Subjective Quality (4.5/5). Moreover, from the classification conducted using the ID3 algorithm, it was observed that 6 out of 11 mHealth applications were preferred for the self-management of T2DM. Full article

(This article belongs to the Special Issue Applications of Machine Learning, Big Data and Internet of Things in Health Management)

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15 pages, 1946 KiB

Open AccessArticle

Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling

by Essam A. Rashed and Akimasa Hirata

Int. J. Environ. Res. Public Health 2021, 18(15), 7799; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18157799 - 22 Jul 2021

Cited by 21 | Viewed by 3692

Abstract

The significant health and economic effects of COVID-19 emphasize the requirement for reliable forecasting models to avoid the sudden collapse of healthcare facilities with overloaded hospitals. Several forecasting models have been developed based on the data acquired within the early stages of the virus spread. However, with the recent emergence of new virus variants, it is unclear how the new strains could influence the efficiency of forecasting using models adopted using earlier data. In this study, we analyzed daily positive cases (DPC) data using a machine learning model to understand the effect of new viral variants on morbidity rates. A deep learning model that considers several environmental and mobility factors was used to forecast DPC in six districts of Japan. From machine learning predictions with training data since the early days of COVID-19, high-quality estimation has been achieved for data obtained earlier than March 2021. However, a significant upsurge was observed in some districts after the discovery of the new COVID-19 variant B.1.1.7 (Alpha). An average increase of 20–40% in DPC was observed after the emergence of the Alpha variant and an increase of up to 20% has been recognized in the effective reproduction number. Approximately four weeks was needed for the machine learning model to adjust the forecasting error caused by the new variants. The comparison between machine-learning predictions and reported values demonstrated that the emergence of new virus variants should be considered within COVID-19 forecasting models. This study presents an easy yet efficient way to quantify the change caused by new viral variants with potential usefulness for global data analysis. Full article

(This article belongs to the Special Issue Applications of Machine Learning, Big Data and Internet of Things in Health Management)

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16 pages, 5436 KiB

Open AccessArticle

Using Bus Ticketing Big Data to Investigate the Behaviors of the Population Flow of Chinese Suburban Residents in the Post-COVID-19 Phase

by Yanbing Bai, Lu Sun, Haoyu Liu and Chao Xie

Int. J. Environ. Res. Public Health 2021, 18(11), 6066; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18116066 - 04 Jun 2021

Cited by 1 | Viewed by 2967

Abstract

Large-scale population movements can turn local diseases into widespread epidemics. Grasping the characteristic of the population flow in the context of the COVID-19 is of great significance for providing information to epidemiology and formulating scientific and reasonable prevention and control policies. Especially in the post-COVID-19 phase, it is essential to maintain the achievement of the fight against the epidemic. Previous research focuses on flight and railway passenger travel behavior and patterns, but China also has numerous suburban residents with a not-high economic level; investigating their travel behaviors is significant for national stability. However, estimating the impacts of the COVID-19 for suburban residents’ travel behaviors remains challenging because of lacking apposite data. Here we submit bus ticketing data including approximately 26,000,000 records from April 2020–August 2020 for 2705 stations. Our results indicate that Suburban residents in Chinese Southern regions are more likely to travel by bus, and travel frequency is higher. Associated with the economic level, we find that residents in the economically developed region more likely to travel or carry out various social activities. Considering from the perspective of the traveling crowd, we find that men and young people are easier to travel by bus; however, they are exactly the main workforce. The indication of our findings is that suburban residents’ travel behavior is affected profoundly by economy and consistent with the inherent behavior patterns before the COVID-19 outbreak. We use typical regions as verification and it is indeed the case. Full article

(This article belongs to the Special Issue Applications of Machine Learning, Big Data and Internet of Things in Health Management)

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Review

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29 pages, 5887 KiB

Open AccessReview

Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining

by Ignacio Rodríguez-Rodríguez, José-Víctor Rodríguez, Niloofar Shirvanizadeh, Andrés Ortiz and Domingo-Javier Pardo-Quiles

Int. J. Environ. Res. Public Health 2021, 18(16), 8578; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18168578 - 13 Aug 2021

Cited by 30 | Viewed by 5485

Abstract

The COVID-19 pandemic has wreaked havoc in every country in the world, with serious health-related, economic, and social consequences. Since its outbreak in March 2020, many researchers from different fields have joined forces to provide a wide range of solutions, and the support for this work from artificial intelligence (AI) and other emerging concepts linked to intelligent data analysis has been decisive. The enormous amount of research and the high number of publications during this period makes it difficult to obtain an overall view of the different applications of AI to the management of COVID-19 and an understanding of how research in this field has been evolving. Therefore, in this paper, we carry out a scientometric analysis of this area supported by text mining, including a review of 18,955 publications related to AI and COVID-19 from the Scopus database from March 2020 to June 2021 inclusive. For this purpose, we used VOSviewer software, which was developed by researchers at Leiden University in the Netherlands. This allowed us to examine the exponential growth in research on this issue and its distribution by country, and to highlight the clear hegemony of the United States (USA) and China in this respect. We used an automatic process to extract topics of research interest and observed that the most important current lines of research focused on patient-based solutions. We also identified the most relevant journals in terms of the COVID-19 pandemic, demonstrated the growing value of open-access publication, and highlighted the most influential authors by means of an analysis of citations and co-citations. This study provides an overview of the current status of research on the application of AI to the pandemic. Full article

(This article belongs to the Special Issue Applications of Machine Learning, Big Data and Internet of Things in Health Management)

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