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eHealth Innovative Approaches and Applications: 2nd Edition
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eHealth Innovative Approaches and Applications: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2178

Special Issue Editors

Dr. Stefano Silvestri

E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking ICAR, National Research Council of Italy (CNR), 00185 Rome, Italy
Interests: parallel computing; natural language processing; artificial intelligence; deep learning; eHealth; big data analytics; cyber physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Gargiulo

E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking ICAR, National Research Council of Italy (CNR), Rome, Italy
Interests: artificial intelligence; deep learning; natural language processing; big data analytics; quantum computing
Special Issues, Collections and Topics in MDPI journals
Dr. Dario Di Stasio

E-Mail Website
Guest Editor
Multidisciplinary Department of Medical-Surgical and Odontostomatological Specialties, University of Campania "Luigi Vanvitelli", 80138 Napoli, Italy
Interests: dentistry; oral medicine; oral pathology; oral immunology; imaging in oral diseases, intraoral ultrasonography, image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Raffaele Guarasci

E-Mail Website
Guest Editor
Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), 80131 Naples, Italy
Interests: quantum computing; machine learning prediction; LSTM; natural language processing; computational linguistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Innovative ICT technologies, approaches and applications are becoming increasingly pervasive in medicine and healthcare. At the same time, physicians and medical professionals are adopting and exploiting these complex and advanced systems based on the latest technologies for their daily routine. Scientific research constantly proposes new approaches and applications with high potential for use in the eHealth sector. It is, therefore, necessary to disseminate these new results and the achieved enhancements in this area to take full advantage of the latest advances in the field of ICT applied to the medical sector.

This Special Issue will focus on innovative approaches and applications for eHealth. In detail, it will consider recent technologies and methodologies applied to medicine and healthcare for the definition of complex systems and architectures in the eHealth domain, such as the Internet of Things (IoT), artificial intelligence (AI), quantum computing (QC), big data analytics (BDA) and cybersecurity (CS). Contributions can focus on architectures, algorithms and methods; survey papers and reviews are also welcome.

The main topics include, but are not limited to, the following:

  • eHealth;
  • Medical informatics;
  • Knowledge managements in eHealth;
  • Big data analytics for eHealth;
  • eHealth big data architectures;
  • Artificial intelligence in medicine;
  • Machine and deep learning approaches for eHealth;
  • Health information systems;
  • Biomedical Internet of Things (IoT) devices;
  • Security and privacy of medical data;
  • Cybersecurity in healthcare;
  • Diagnosis and therapy support systems;
  • Quantum computing approaches for eHealth;
  • Quantum computing for drug discovery.

Dr. Stefano Silvestri
Dr. Francesco Gargiulo
Dr. Dario Di Stasio
Dr. Raffaele Guarasci
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • eHealth
  • medical informatics
  • big data analytics for eHealth
  • eHealth big data architectures
  • artificial intelligence in medicine
  • machine and deep learning approaches for eHealth
  • health information systems
  • biomedical Internet of Things (IoT) devices
  • security and privacy of medical data
  • cybersecurity in healthcare
  • diagnosis and therapy support systems
  • quantum computing approaches for eHealth
  • quantum computing for drug discovery

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Related Special Issue

Published Papers (2 papers)

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Research

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12 pages, 1369 KiB  
Article
Optimized Prescreen Survey Tool for Predicting Sleep Apnea Based on Deep Neural Network: Pilot Study
by Jungyoon Kim, Jaehyun Park, Jangwoon Park and Salim Surani
Appl. Sci. 2024, 14(17), 7608; https://doi.org/10.3390/app14177608 - 28 Aug 2024
Viewed by 701
Abstract
Obstructive sleep apnea (OSA) is one of the common sleep disorders related to breathing. It is important to identify an optimal set of questions among the existing questionnaires, using a data-driven approach, that can prescreen OSA with high sensitivity and specificity. The current [...] Read more.
Obstructive sleep apnea (OSA) is one of the common sleep disorders related to breathing. It is important to identify an optimal set of questions among the existing questionnaires, using a data-driven approach, that can prescreen OSA with high sensitivity and specificity. The current study proposes reliable models that are based on machine learning techniques to predict the severity of OSA. A total of 66 participants consisted of 45 males and 21 females (average age = 52.4 years old; standard deviation ± 14.6). Participants were asked to fill out the questionnaire items. If the value of the Respiratory Disturbance Index (RDI) was more than 30, the participant was diagnosed with severe OSA. Several different modeling techniques were applied, including deep neural networks with a scaled principal component analysis (DNN-PCA), random forest (RF), Adaptive Boosting Classifier (ABC), Decision Tree Classifier (DTC), K-nearest neighbors classifier (KNC), and support vector machine classifier (SVMC). Among the participants, 27 participants were diagnosed with severe OSA (RDI > 30). The area under the receiver operating characteristic curve (AUROC) was used to evaluate the developed models. As a result, the AUROC values of DNN-PCA, RF, ABC, DTC, KNC, and SVMC models were 0.95, 0.62, 0.53, 0.53, 0.51, and 0.78, respectively. The highest AUROC value was found in the DNN-PCA model with a sensitivity of 0.95, a specificity of 0.75, a positive predictivity of 0.95, an F1 score of 0.95, and an accuracy of 0.95. The DNN-PCA model outperforms the existing screening questionnaires, scores, and other models. Full article
(This article belongs to the Special Issue eHealth Innovative Approaches and Applications: 2nd Edition)
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Review

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16 pages, 338 KiB  
Review
Key Factors for a Successful Telemedicine Solution for Cardiovascular Diseases: A Systematic Review
by Giuseppe Felice Russo, Ilaria Basile, Mario Ciampi and Stefano Silvestri
Appl. Sci. 2024, 14(17), 7633; https://doi.org/10.3390/app14177633 - 29 Aug 2024
Viewed by 1031
Abstract
Background: Telemonitoring systems in cardiology have shown potential in improving chronic cardiovascular disease (CVD) management. This study aims to evaluate the impact of telemonitoring, mainly through mobile applications, on patient outcomes such as self-care, blood pressure control, quality of life, and hospitalization. Methods: [...] Read more.
Background: Telemonitoring systems in cardiology have shown potential in improving chronic cardiovascular disease (CVD) management. This study aims to evaluate the impact of telemonitoring, mainly through mobile applications, on patient outcomes such as self-care, blood pressure control, quality of life, and hospitalization. Methods: We systematically reviewed studies assessing telemonitoring methods for patients with chronic CVD. The analysis included studies from various geographic regions and healthcare settings, focusing on qualitative outcomes without performing a meta-analysis. Results: Telemonitoring was found to aid in maintaining blood pressure and significantly enhance self-care abilities. Improvements in quality of life were observed in some studies, though results varied. Most studies indicated telemonitoring could effectively manage blood pressure and reduce hypertension-related complications. However, the heterogeneity of interventions and outcomes measured across trials posed challenges for a comprehensive meta-analysis. Conclusions: Integrating telemonitoring systems into routine care can significantly improve disease management and patient outcomes for chronic CVD patients. Future research should standardize telemonitoring interventions and outcome measures, conduct long-term studies, and evaluate the cost-effectiveness of these systems. Greater blindness in future randomized controlled trials and more studies on atrial fibrillation are also necessary. Significant potential exists for telemonitoring to improve patient outcomes and assist in managing chronic illnesses. Full article
(This article belongs to the Special Issue eHealth Innovative Approaches and Applications: 2nd Edition)
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