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Enhancement of Public Health Professionals via Signal Processing, Machine Learning, Artificial Intelligence and Bioinformatics

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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 (31 August 2023) | Viewed by 19398

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Dr. Barbara Calabrese

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Data Analytics Research Center, Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy
Interests: signal processing; biomedical data analysis; brain computer interface
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mario Cannataro

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Department of Medical and Surgical Sciences, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
Interests: bioinformatics, parallel computing, artificial intelligence, data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The applications of signal processing, machine learning, artificial intelligence and bioinformatics in medicine and public health are aimed to promote the health of the whole population and individuals by offering innovative and effective services. The development of predictive models trained through the big data available in healthcare may help foster healthy behaviors in the population and prevent diseases by diminishing the risks conditions of the people. The extension of classical Electronic Patient Records with genomic information enables personalized medicine that introduces bioinformatics analysis in clinical practice. The integrated analysis of heterogeneous data, such as infections and vaccinations rates, climate and pollution data, tracing of people, may help the management of the COVID-19 pandemics, can improve pandemic surveillance, and may support political decisions.

Signal processing, machine learning, artificial intelligence, and bioinformatics methods lead to novel e-Health applications central in public health that can accurately interpret biomedical, biological, and population data and are empowering both medicine and healthcare, facing the challenges posed by public health. In addition, the current pandemic has emphasized the importance of health informatics and computer science in healthcare.

This Special Issue invites submissions from public health professionals, medical and health informatics experts, data scientists, bioinformaticians, biologists, and medical doctors and epidemiologists to present novel methods and applications to improve the public health profession. Interdisciplinary approaches and applications for fighting the COVID-19 pandemic are also welcomed. Topics including the application of Machine Learning, Artificial Intelligence, Data Mining, Informatics, Bioinformatics, Biostatistics, Big Data, in the following fields are welcomed:

Public Health;
Public Health Professionals;
Public health informatics;
Health information systems;
Pandemic surveillance;
COVID-19 surveillance;
E-Health.

Dr. Barbara Calabrese
Prof. Dr. Mario Cannataro
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.

Published Papers (8 papers)

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Research

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21 pages, 1532 KiB

Open AccessArticle

Scoliosis Management through Apps and Software Tools

by Lorella Bottino, Marzia Settino, Luigi Promenzio and Mario Cannataro

Int. J. Environ. Res. Public Health 2023, 20(8), 5520; https://doi.org/10.3390/ijerph20085520 - 14 Apr 2023

Cited by 1 | Viewed by 3146

Abstract

Background: Scoliosis is curvature of the spine, often found in adolescents, which can impact on quality of life. Generally, scoliosis is diagnosed by measuring the Cobb angle, which represents the gold standard for scoliosis grade quantification. Commonly, scoliosis evaluation is conducted in person by medical professionals using traditional methods (i.e., involving a scoliometer and/or X-ray radiographs). In recent years, as has happened in various medicine disciplines, it is possible also in orthopedics to observe the spread of Information and Communications Technology (ICT) solutions (i.e., software-based approaches). As an example, smartphone applications (apps) and web-based applications may help the doctors in screening and monitoring scoliosis, thereby reducing the number of in-person visits. Objectives: This paper aims to provide an overview of the main features of the most popular scoliosis ICT tools, i.e., apps and web-based applications for scoliosis diagnosis, screening, and monitoring. Several apps are assessed and compared with the aim of providing a valid starting point for doctors and patients in their choice of software-based tools. Benefits for the patients may be: reducing the number of visits to the doctor, self-monitoring of scoliosis. Benefits for the doctors may be: monitoring the scoliosis progression over time, managing several patients in a remote way, mining the data of several patients for evaluating different therapeutic or exercise prescriptions. Materials and Methods: We first propose a methodology for the evaluation of scoliosis apps in which five macro-categories are considered: (i) technological aspects (e.g., available sensors, how angles are measured); (ii) the type of measurements (e.g., Cobb angle, angle of trunk rotation, axial vertebral rotation); (iii) availability (e.g., app store and eventual fee to pay); (iv) the functions offered to the user (e.g., posture monitoring, exercise prescription); (v) overall evaluation (e.g., pros and cons, usability). Then, six apps and one web-based application are described and evaluated using this methodology. Results: The results for assessment of scoliosis apps are shown in a tabular format for ease of understanding and intuitive comparison, which can help the doctors, specialists, and families in their choice of scoliosis apps. Conclusions: The use of ICT solutions for spinal curvature assessment and monitoring brings several advantages to both patients and orthopedics specialists. Six scoliosis apps and one web-based application are evaluated, and a guideline for their selection is provided. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Signal Processing, Machine Learning, Artificial Intelligence and Bioinformatics)

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26 pages, 2379 KiB

Open AccessArticle

Development and Performance Evaluation of an IoT-Integrated Breath Analyzer

by Abd Alghani Khamis, Aida Idris, Abdallah Abdellatif, Noor Ashikin Mohd Rom, Taha Khamis, Mohd Sayuti Ab Karim, Shamini Janasekaran and Rusdi Bin Abd Rashid

Int. J. Environ. Res. Public Health 2023, 20(2), 1319; https://doi.org/10.3390/ijerph20021319 - 11 Jan 2023

Cited by 3 | Viewed by 1965

Abstract

Although alcohol consumption may produce effects that can be beneficial or harmful, alcohol consumption prevails among communities around the globe. Additionally, alcohol consumption patterns may be associated with several factors among communities and individuals. Numerous technologies and methods are implemented to enhance the detection and tracking of alcohol consumption, such as vehicle-integrated and wearable devices. In this paper, we present a cellular-based Internet of Things (IoT) implementation in a breath analyzer to enable data collection from multiple users via a single device. Cellular technology using hypertext transfer protocol (HTTP) was implemented as an IoT gateway. IoT integration enabled the direct retrieval of information from a database relative to the device and direct upload of data from the device onto the database. A manually developed threshold algorithm was implemented to quantify alcohol concentrations within a range from 0 to 200 mcg/100 mL breath alcohol content using electrochemical reactions in a fuel-cell sensor. Two data collections were performed: one was used for the development of the model and was split into two sets for model development and on-machine validation, and another was used as an experimental verification test. An overall accuracy of 98.16% was achieved, and relative standard deviations within the range from 1.41% to 2.69% were achieved, indicating the reliable repeatability of the results. The implication of this paper is that the developed device (an IoT-integrated breath analyzer) may provide practical assistance for healthcare representatives and researchers when conducting studies involving the detection and data collection of alcohol consumption patterns. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Signal Processing, Machine Learning, Artificial Intelligence and Bioinformatics)

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9 pages, 601 KiB

Open AccessArticle

Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects

by Michele Lo Giudice, Edoardo Ferlazzo, Nadia Mammone, Sara Gasparini, Vittoria Cianci, Angelo Pascarella, Anna Mammì, Danilo Mandic, Francesco Carlo Morabito and Umberto Aguglia

Int. J. Environ. Res. Public Health 2022, 19(23), 15733; https://doi.org/10.3390/ijerph192315733 - 26 Nov 2022

Cited by 3 | Viewed by 1397

Abstract

Identifying subjects with epileptic seizures or psychogenic non-epileptic seizures from healthy subjects via interictal EEG analysis can be a very challenging issue. Indeed, at visual inspection, EEG can be normal in both cases. This paper proposes an automatic diagnosis approach based on deep learning to differentiate three classes: subjects with epileptic seizures (ES), subjects with non-epileptic psychogenic seizures (PNES) and control subjects (CS), analyzed by non-invasive low-density interictal scalp EEG recordings. The EEGs of 42 patients with new-onset ES, 42 patients with PNES video recorded and 19 patients with CS all with normal interictal EEG on visual inspection, were analyzed in the study; none of them was taking psychotropic drugs before registration. The processing pipeline applies empirical mode decomposition (EMD) to 5s EEG segments of 19 channels in order to extract enhanced features learned automatically from the customized convolutional neural network (CNN). The resulting CNN has been shown to perform well during classification, with an accuracy of 85.7%; these results encourage the use of deep processing systems to assist clinicians in difficult clinical settings. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Signal Processing, Machine Learning, Artificial Intelligence and Bioinformatics)

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14 pages, 1217 KiB

Open AccessArticle

A Depression-Risk Mental Pattern Identified by Hidden Markov Model in Undergraduates

by Xiaowei Jiang, Yanan Chen, Na Ao, Yang Xiao and Feng Du

Int. J. Environ. Res. Public Health 2022, 19(21), 14411; https://doi.org/10.3390/ijerph192114411 - 03 Nov 2022

Cited by 1 | Viewed by 1491

Abstract

Few studies have examined depression risk screening approaches. Universal depression screening in youth typically focuses on directly measuring the current distress and impairment by several kinds of depression rating scales. However, as many people have stigmatizing attitudes to individuals with depression, youths with depression were in fear of being known, and embarrassment held them back from reporting their depression symptoms. Thus, the present study aimed to identify the best, most easy access screening approach for indirectly predicting depression risks in undergraduates. Here, the depression score was ranked and viewed as the different stages in the development of depression; then, we used a Hidden Markov Model (HMM) approach to identify depression risks. Participants included 1247 undergraduates (female = 720, mean age = 19.86 years (std =1.31), from 17 to 25) who independently completed inventories for depressive symptoms, emotion regulation, subjective well-being (life satisfaction, negative and positive affect), and coping styles (positive and negative). Our findings indicated that the risk pattern (state 1) and the health pattern (state 2) showed distinct different rating results in emotional regulation, subjective well-being, and coping style. Screening for prospective risk of depression can be better accomplished by HMM incorporating subjective well-being, emotion regulation, and coping style. This study discussed the implications for future research and evidence-based decision-making for depression screening initiatives. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Signal Processing, Machine Learning, Artificial Intelligence and Bioinformatics)

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19 pages, 949 KiB

Open AccessArticle

Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

by Mengting Cheng, Xianmiao Li and Jicheng Xu

Int. J. Environ. Res. Public Health 2022, 19(20), 13311; https://doi.org/10.3390/ijerph192013311 - 15 Oct 2022

Cited by 11 | Viewed by 2962

Abstract

Artificial intelligence (AI)-assisted diagnosis and treatment could expand the medical scenarios and augment work efficiency and accuracy. However, factors influencing healthcare workers’ adoption intention of AI-assisted diagnosis and treatment are not well-understood. This study conducted a cross-sectional study of 343 dental healthcare workers from tertiary hospitals and secondary hospitals in Anhui Province. The obtained data were analyzed using structural equation modeling. The results showed that performance expectancy and effort expectancy were both positively related to healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Social influence and human–computer trust, respectively, mediated the relationship between expectancy (performance expectancy and effort expectancy) and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Furthermore, social influence and human–computer trust played a chain mediation role between expectancy and healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Our study provided novel insights into the path mechanism of healthcare workers’ adoption intention of AI-assisted diagnosis and treatment. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Signal Processing, Machine Learning, Artificial Intelligence and Bioinformatics)

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Review

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14 pages, 674 KiB

Open AccessReview

Swallow Detection with Acoustics and Accelerometric-Based Wearable Technology: A Scoping Review

by Bryan Pak-Hei So, Tim Tin-Chun Chan, Liangchao Liu, Calvin Chi-Kong Yip, Hyo-Jung Lim, Wing-Kai Lam, Duo Wai-Chi Wong, Daphne Sze Ki Cheung and James Chung-Wai Cheung

Int. J. Environ. Res. Public Health 2023, 20(1), 170; https://doi.org/10.3390/ijerph20010170 - 22 Dec 2022

Cited by 7 | Viewed by 2771

Abstract

Swallowing disorders, especially dysphagia, might lead to malnutrition and dehydration and could potentially lead to fatal aspiration. Benchmark swallowing assessments, such as videofluoroscopy or endoscopy, are expensive and invasive. Wearable technologies using acoustics and accelerometric sensors could offer opportunities for accessible and home-based long-term assessment. Identifying valid swallow events is the first step before enabling the technology for clinical applications. The objective of this review is to summarize the evidence of using acoustics-based and accelerometric-based wearable technology for swallow detection, in addition to their configurations, modeling, and assessment protocols. Two authors independently searched electronic databases, including PubMed, Web of Science, and CINAHL. Eleven (n = 11) articles were eligible for review. In addition to swallowing events, non-swallowing events were also recognized by dry (saliva) swallowing, reading, yawning, etc., while some attempted to classify the types of swallowed foods. Only about half of the studies reported that the device attained an accuracy level of >90%, while a few studies reported poor performance with an accuracy of <60%. The reviewed articles were at high risk of bias because of the small sample size and imbalanced class size problem. There was high heterogeneity in assessment protocol that calls for standardization for swallowing, dry-swallowing and non-swallowing tasks. There is a need to improve the current wearable technology and the credibility of relevant research for accurate swallowing detection before translating into clinical screening for dysphagia and other swallowing disorders. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Signal Processing, Machine Learning, Artificial Intelligence and Bioinformatics)

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16 pages, 812 KiB

Open AccessReview

Applications and Current Medico-Legal Challenges of Telemedicine in Ophthalmology

by Daniela Mazzuca, Massimiliano Borselli, Santo Gratteri, Giovanna Zampogna, Alessandro Feola, Marcello Della Corte, Francesca Guarna, Vincenzo Scorcia and Giuseppe Giannaccare

Int. J. Environ. Res. Public Health 2022, 19(9), 5614; https://doi.org/10.3390/ijerph19095614 - 05 May 2022

Cited by 12 | Viewed by 3191

Abstract

Background: The digital revolution is redesigning the healthcare model, and telemedicine offers a good example of the best cost-effectiveness ratio. The COVID-19 pandemic has catalysed the use of the telemedicine. The aim of this review is to describe and discuss the role and the main applications of telemedicine in the ophthalmic clinical practice as well as the related medico-legal aspects. Methods: 45 original articles and 5 reviews focused on this topic and published in English language from 1997 and 2021 were searched on the online databases of Pubmed, Scopus, Web of Sciences and Embase, by using the following key words: “telemedicine”, “privacy”, “ophthalmology”, “COVID-19” and “informed consent”. Results: Telemedicine is able to guarantee patient care using information and communication technologies. Technology creates an opportunity to link doctors with the aim of assessing clinical cases and maintaining high standards of care while performing and saving time as well. Ophthalmology is one of the fields in which telemedicine is most commonly used for patient management. Conclusions: Telemedicine offers benefits to patients in terms of saving time and costs and avoiding physical contact; however, it is necessary to point out significant limitations such as the absence of physical examinations, the possibility of transmission failure and potential violations of privacy and confidentiality. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Signal Processing, Machine Learning, Artificial Intelligence and Bioinformatics)

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Other

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5 pages, 285 KiB

Open AccessProtocol

“What Are the Applications for Remote Rehabilitation Management in Cystic Fibrosis?”: A Scoping Review Protocol

by Matteo De Marchis, Matteo Cioeta and Mario Cannataro

Int. J. Environ. Res. Public Health 2022, 19(21), 14014; https://doi.org/10.3390/ijerph192114014 - 27 Oct 2022

Viewed by 1275

Abstract

Background: Telemedicine is an effective, widely used strategy in the field of cystic fibrosis management. The objective of this scoping review is to summarize and analyze the scientific literature with the special focus on the tools and the strategies used in patients with a chronic disease, such as cystic fibrosis. Methods: This scoping review will be performed in accordance with the Joanna Briggs Institute methodology. In this context, the planned scoping review is a research synthesis that will map the literature on the applications of telemedicine and telemonitoring to the management of cystic fibrosis, with the aim to identify key concepts in the research and work to be conducted that may impact clinical practice. Studies will be included if they meet the following population, concept, and context criteria: all patients with cystic fibrosis receiving treatment with the tools of telemedicine and telemonitoring. No study design, publication type, or data restrictions will be applied. MEDLINE, Scopus, CINHAL, Pedro, Embase, Web of Science, ACM Digital Library, Health Technology Assessment Database (HTA), and Cochrane Central will be searched up to September 2022. Discussion: To the best of our knowledge, this will be the first scoping review to provide a comprehensive overview of the topic. The results could add meaningful information for future research and, especially, for clinical practice, when implementing telerehabilitation in cystic fibrosis treatment. Furthermore, we expect that our work may identify possible knowledge gaps on the topic. The results of this research will be published in a peer-reviewed journal and will be presented at relevant international scientific events, such as in congress or meetings. Full article

(This article belongs to the Special Issue Enhancement of Public Health Professionals via Signal Processing, Machine Learning, Artificial Intelligence and Bioinformatics)

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