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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 37080

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Special Issue Editors

Dr. Stefano Silvestri

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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

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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

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 welcomed.

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

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.

Dr. Stefano Silvestri
Dr. Francesco Gargiulo
Guest Editors

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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.

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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

eHealth Innovative Approaches and Applications: 2nd Edition in Applied Sciences (2 articles)

Published Papers (14 papers)

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Editorial

Jump to: Research, Review

9 pages, 201 KiB

Open AccessEditorial

Special Issue on eHealth Innovative Approaches and Applications

by Stefano Silvestri and Francesco Gargiulo

Appl. Sci. 2024, 14(6), 2571; https://doi.org/10.3390/app14062571 - 19 Mar 2024

Viewed by 1204

Abstract

Innovative ICT technologies, approaches and applications are becoming increasingly pervasive in several domains, including in medicine and healthcare [...] Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

Research

Jump to: Editorial, Review

17 pages, 385 KiB

Open AccessArticle

Integrating PubMed Label Hierarchy Knowledge into a Complex Hierarchical Deep Neural Network

by Stefano Silvestri, Francesco Gargiulo and Mario Ciampi

Appl. Sci. 2023, 13(24), 13117; https://doi.org/10.3390/app132413117 - 9 Dec 2023

Cited by 1 | Viewed by 1366

Abstract

This paper proposes an innovative method that exploits a complex deep learning network architecture, called Hierarchical Deep Neural Network (HDNN), specifically developed for the eXtreme Multilabel Text Classification (XMTC) task, when the label set is hierarchically organized, such as the case of the PubMed article labeling task. In detail, the topology of the proposed HDNN architecture follows the exact hierarchical structure of the label set to integrate this knowledge directly into the DNN. We assumed that if a label set hierarchy is available, as in the case of the PubMed Dataset, forcing this information into the network topology could enhance the classification performances and the interpretability of the results, especially related to the hierarchy. We performed an experimental assessment of the PubMed article classification task, demonstrating that the proposed HDNN provides performance improvement for a baseline based on a classic flat Convolution Neural Network (CNN) deep learning architecture, in particular in terms of hierarchical measures. These results provide useful hints for integrating previous and innate knowledge in a deep neural network. The drawback of the HDNN is the high computational time required to train the neural network, which can be addressed with a parallel implementation planned as a future work. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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14 pages, 702 KiB

Open AccessArticle

Bridging Theory and Practice: An Innovative Approach to Android Programming Education through Nutritional Application Development and Problem-Based Learning

by Inigo Lopez-Gazpio

Appl. Sci. 2023, 13(22), 12140; https://doi.org/10.3390/app132212140 - 8 Nov 2023

Viewed by 1077

Abstract

This study introduces an innovative Problem-Based Learning (PBL) methodology to enhance the teaching of Android programming, focusing on addressing nutritional challenges. Conducted within the Computer Science degree at the University of Deusto, this research engages third-year students in developing applications aimed at improving access to nutritional knowledge. The novelty of this approach lies in its integration of advanced programming concepts with practical application development, fostering a deeper understanding and engagement among students. The applications enable users to access detailed nutritional information from open-access food databases, catering to individuals with specific dietary constraints. Preliminary results indicate a significant improvement in student engagement and learning outcomes compared to traditional teaching methods, underscoring the potential of this methodology in fostering future researchers and advancing educational practices in computer science. This research contributes to the field by demonstrating the efficacy of combining PBL with application development in enhancing learning experiences and outcomes in programming education. Our findings not only contribute valuable insights into the unique challenges and motivators associated with Android programming but also pave the way for tailored educational strategies that can optimize the learning experience in this domain. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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18 pages, 9473 KiB

Open AccessArticle

Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN

by Abdullah Alourani, Muhammad Sardaraz, Muhammad Tahir and Muhammad Saud Khan

Appl. Sci. 2023, 13(21), 11840; https://doi.org/10.3390/app132111840 - 29 Oct 2023

Viewed by 1518

Abstract

The Internet of Medical Things (IoMT) is the network of medical devices, hardware infrastructure, and software applications used to connect the healthcare information technology. Massive traffic growth and user expectations cause challenges in the current exhausting models of IoMT data. To reduce the IoMT traffic, Information Centric Network (ICN) is a suitable technique. ICN uses persistent naming multicast communication that reduces the response time. ICN in IoMT provides a promising feature to reduce the overhead due to the distribution of commonly accessed contents. Some parameters such as energy consumption, communication cost, etc., influence the performance of sensors in the IoMT network. Excessive and unbalanced energy consumption degrades the network performance and lifetime. This article presents a framework called Dynamic Cache Scheme (DCS) that implements energy-efficient cache scheduling in IoMT over ICN to reduce network traffic. The proposed framework establishes a balance between the multi-hop traffic and data item freshness. The technique improves the freshness of data; thus, updated data are provided to the end-users via the effective utilization of caching in IoMT. The proposed framework is tested on important parameters, i.e., cache-hit-ratio, stretch, and content retrieval latency. The results obtained are compared with the state-of-the-art models. Results’ analysis shows that the proposed framework outperforms the compared models in terms of cache-hit-ratio, stretch, and content retrieval latency by 59.42%, 32.66%, and 18.8%, respectively. In the future, it is intended to explore the applicability of DCS in more scenarios and optimize further. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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13 pages, 635 KiB

Open AccessArticle

Machine Learning for COVID-19 and Influenza Classification during Coexisting Outbreaks

by Iris Viana dos Santos Santana, Álvaro Sobrinho, Leandro Dias da Silva and Angelo Perkusich

Appl. Sci. 2023, 13(20), 11518; https://doi.org/10.3390/app132011518 - 20 Oct 2023

Viewed by 1366

Abstract

This study compares the performance of machine learning models for selecting COVID-19 and influenza tests during coexisting outbreaks in Brazil, avoiding the waste of resources in healthcare units. We used COVID-19 and influenza datasets from Brazil to train the Decision Tree (DT), Multilayer Perceptron (MLP), Gradient Boosting Machine (GBM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbors, Support Vector Machine (SVM), and Logistic Regression algorithms. Moreover, we tested the models using the 10-fold cross-validation method to increase confidence in the results. During the experiments, the GBM, DT, RF, XGBoost, and SVM models showed the best performances, with similar results. The high performance of tree-based models is relevant for the classification of COVID-19 and influenza because they are usually easier to interpret, positively impacting the decision-making of health professionals. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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22 pages, 517 KiB

Open AccessArticle

Cybersecurity and Medical Imaging: A Simulation-Based Approach to DICOM Communication

by Stylianos Karagiannis, Emmanouil Magkos, Christoforos Ntantogian, Ricardo Cabecinha and Theofanis Fotis

Appl. Sci. 2023, 13(18), 10072; https://doi.org/10.3390/app131810072 - 6 Sep 2023

Cited by 3 | Viewed by 2928

Abstract

Medical imaging plays a crucial role in modern healthcare, providing essential information for accurate diagnosis and treatment planning. The Digital Imaging and Communications in Medicine (DICOM) standard has revolutionized the storage, transmission, and sharing of medical images and related data. Despite its advantages, implementation and deployment of the DICOM protocol often suffers from incomplete understanding, leading to vulnerabilities within the healthcare ecosystem. This research paper presents an implementation of DICOM communication and the development of a practical demonstration for simulation purposes The simulation can be used for conducting cybersecurity tests in the context of DICOM communication. Overall, the simulation provides a digital environment that can help in retrieving valuable insights into the practical aspects of DICOM communication and PACS integration, serving as a valuable resource for medical imaging professionals, researchers, and developers. These research results provide practical insights, and the DICOM simulation can be used in realistic contexts to showcase a variety of security scenarios. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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16 pages, 12188 KiB

Open AccessArticle

Leveraging Language Models for Inpatient Diagnosis Coding

by Kerdkiat Suvirat, Detphop Tanasanchonnakul, Sawrawit Chairat and Sitthichok Chaichulee

Appl. Sci. 2023, 13(16), 9450; https://doi.org/10.3390/app13169450 - 21 Aug 2023

Cited by 1 | Viewed by 1866

Abstract

Medical coding plays an essential role in medical billing, health resource planning, clinical research and quality assessment. Automated coding systems offer promising solutions to streamline the coding process, improve accuracy and reduce the burden on medical coders. To date, there has been limited research focusing on inpatient diagnosis coding using an extensive comprehensive dataset and encompassing the full ICD-10 code sets. In this study, we investigate the use of language models for coding inpatient diagnoses and examine their performance using an institutional dataset comprising 230,645 inpatient admissions and 8677 diagnosis codes spanning over a six-year period. A total of three language models, including two general-purpose models and a domain-specific model, were evaluated and compared. The results show competitive performance among the models, with the domain-specific model achieving the highest micro-averaged F1 score of 0.7821 and the highest mean average precision of 0.8097. Model performance varied by disease and condition, with diagnosis codes with larger sample sizes producing better results. The rarity of certain diseases and conditions posed challenges to accurate coding. The results also indicated the potential difficulties of the model with long clinical documents. Our models demonstrated the ability to capture relevant associations between diagnoses. This study advances the understanding of language models for inpatient diagnosis coding and provides insights into the extent to which the models can be used. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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26 pages, 740 KiB

Open AccessArticle

Challenges and Opportunities for Conducting Dynamic Risk Assessments in Medical IoT

by Ricardo M. Czekster, Paul Grace, César Marcon, Fabiano Hessel and Silvio C. Cazella

Appl. Sci. 2023, 13(13), 7406; https://doi.org/10.3390/app13137406 - 22 Jun 2023

Cited by 9 | Viewed by 3205

Abstract

Modern medical devices connected to public and private networks require additional layers of communication and management to effectively and securely treat remote patients. Wearable medical devices, for example, can detect position, movement, and vital signs; such data help improve the quality of care for patients, even when they are not close to a medical doctor or caregiver. In healthcare environments, these devices are called Medical Internet-of-Things (MIoT), which have security as a critical requirement. To protect users, traditional risk assessment (RA) methods can be periodically carried out to identify potential security risks. However, such methods are not suitable to manage sophisticated cyber-attacks happening in near real-time. That is the reason why dynamic RA (DRA) approaches are emerging to tackle the inherent risks to patients employing MIoT as wearable devices. This paper presents a systematic literature review of RA in MIoT that analyses the current trends and existing approaches in this field. From our review, we first observe the significant ways to mitigate the impact of unauthorised intrusions and protect end-users from the leakage of personal data and ensure uninterrupted device usage. Second, we identify the important research directions for DRA that must address the challenges posed by dynamic infrastructures and uncertain attack surfaces in order to better protect users and thwart cyber-attacks before they harm personal (e.g., patients’ home) and institutional (e.g., hospital or health clinic) networks. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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19 pages, 1326 KiB

Open AccessArticle

MIDOM—A DICOM-Based Medical Image Communication System

by Branimir Pervan, Sinisa Tomic, Hana Ivandic and Josip Knezovic

Appl. Sci. 2023, 13(10), 6075; https://doi.org/10.3390/app13106075 - 15 May 2023

Cited by 3 | Viewed by 1862

Abstract

Despite the existing medical infrastructure being limited in terms of interoperability, the amount of medical multimedia transferred over the network and shared through various channels increases rapidly. In search of consultations with colleagues, medical professionals with the consent of their patients, usually exchange medical multimedia, mainly in the form of images, by using standard instant messaging services which utilize lossy compression algorithms. That consultation paradigm can easily lead to losses in image representation that can be misinterpreted and lead to the wrong diagnosis. This paper presents MIDOM—Medical Imaging and Diagnostics on the Move, a DICOM-based medical image communication system enhanced with a couple of variants of our previously developed custom lossless Classification and Blending Predictor Coder (CBPC) compression method. The system generally exploits the idea that end devices used by the general population and medical professionals alike are satisfactorily performant and energy-efficient, up to a point to support custom and complex compression methods successfully. The system has been implemented and appropriately integrated with Orthanc, a lightweight DICOM server, and a medical images storing PACS server. We benchmarked the system thoroughly with five real-world anonymized medical image sets in terms of compression ratios and latency reduction, aiming to simulate scenarios in which the availability of the medical services might be hardly reachable or in other ways limited. The results clearly show that our system enhanced with the compression methods in the question pays off in nearly every testing scenario by lowering the network latency to at least 60% of the latency required to send raw and uncompressed image sets and 25% in the best-case, while maintaining the perfect reconstruction of medical images and, thus, providing a more suitable environment for healthcare applications. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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24 pages, 1082 KiB

Open AccessArticle

Chidroid: A Mobile Android Application for Log Collection and Security Analysis in Healthcare and IoMT

by Stylianos Karagiannis, Luís Landeiro Ribeiro, Christoforos Ntantogian, Emmanouil Magkos and Luís Miguel Campos

Appl. Sci. 2023, 13(5), 3061; https://doi.org/10.3390/app13053061 - 27 Feb 2023

Cited by 8 | Viewed by 4424

Abstract

The Internet of Medical Things (IoMT) is a growing trend that has led to the use of connected devices, known as the Internet of Health. The healthcare domain has been a target of cyberattacks, especially with a large number of IoMT devices connected to hospital networks. This factor could allow attackers to access patients’ personal health information (PHI). This research paper proposes Chidroid, an innovative mobile Android application that can retrieve, collect, and distribute logs from smart healthcare devices. The proposed approach enables the creation of datasets, allowing non-structured data to be parsed into semi-structured or structured data that can be used for machine learning and deep learning, and the proposed approach can serve as a universal policy-based tool to examine and analyse security issues in most recent Android versions by distributing logs for analysis. The validation tests demonstrated that the application could retrieve logs and system metrics from various assets and devices in an efficient manner. The collected logs can provide visibility into the device’s activities and help to detect and mitigate potential security risks. This research introduces a way to perform a security analysis on Android devices that uses minimal system resources and reduces battery consumption by pushing the analysis stage to the edge. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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17 pages, 407 KiB

Open AccessArticle

A Maximum-Entropy Fuzzy Clustering Approach for Cancer Detection When Data Are Uncertain

by Mario Fordellone, Ilaria De Benedictis, Dario Bruzzese and Paolo Chiodini

Appl. Sci. 2023, 13(4), 2191; https://doi.org/10.3390/app13042191 - 8 Feb 2023

Cited by 1 | Viewed by 1481

Abstract

(1) Background: Cancer is a leading cause of death worldwide and each year, approximately 400,000 children develop cancer. Early detection of cancer greatly increases the chances for successful treatment, while screening aims to identify individuals with findings suggestive of specific cancer or pre-cancer before they have developed symptoms. Precise detection, however, often mainly relies on human experience and this could suffer from human error and error with a visual inspection. (2) Methods: The research of statistical approaches to analyze the complex structure of data is increasing. In this work, an entropy-based fuzzy clustering technique for interval-valued data (EFC-ID) for cancer detection is suggested. (3) Results: The application on the Breast dataset shows that EFC-ID performs better than the conventional FKM in terms of AUC value (EFC-ID = 0.96, FKM = 0.88), sensitivity (EFC-ID = 0.90, FKM = 0.64), and specificity (EFC-ID = 0.93, FKM = 0.92). Furthermore, the application on the Multiple Myeloma data shows that EFC-ID performs better than the conventional FKM in terms of Chi-squared (EFC-ID = 91.64, FKM = 88.26), Accuracy rate (EFC-ID = 0.71, FKM = 0.60), and Adjusted Rand Index (EFC-ID = 0.33, FKM = 0.21). (4) Conclusions: In all cases, the proposed approach has shown good performance in identifying the natural partition and the advantages of the use of EFC-ID have been detailed illustrated. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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Review

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14 pages, 631 KiB

Open AccessReview

Fractal Analysis Applied to the Diagnosis of Oral Cancer and Oral Potentially Malignant Disorders: A Comprehensive Review

by Maria Contaldo, Federica Di Spirito, Maria Pia Di Palo, Alessandra Amato, Fausto Fiori and Rosario Serpico

Appl. Sci. 2024, 14(2), 777; https://doi.org/10.3390/app14020777 - 16 Jan 2024

Cited by 1 | Viewed by 1495

Abstract

In nature, everything is regular and orderly arranged. The degree of derailment from geometry is related to the disarrangement of living tissues associated with diseases. In the diagnostic field, fractal analysis calculates the fractal dimension (FD), a numerical measure of the degree of regularity of a tissue or structure. As for oral lesions, fractal analysis has been reported to determine the degree of irregular tissue/vascularization derailment mathematically, and this event has been correlated with the nature of the lesion. The purpose of this paper is to evaluate the scientific literature on the fractal analysis of oral cancer and its precursors (oral potentially malignant disorders, OPMDs) to convey whether the specific fractal dimension may be predictive of cancer or the cancerous progression of OPMDs. For this purpose, three databases (PubMed, Scopus, and ISI Web of Science) were investigated according to the PRISMA checklist to answer the following query: “Is fractal analysis a support method to diagnose oral cancer and distinguish it from its precursors?” The risk of biases was also assessed. All original articles published in English were considered; letters, reviews, editorials, and proceedings were excluded. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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16 pages, 950 KiB

Open AccessReview

The Importance of Conceptualising the Human-Centric Approach in Maintaining and Promoting Cybersecurity-Hygiene in Healthcare 4.0

by Kitty Kioskli, Theofanis Fotis, Sokratis Nifakos and Haralambos Mouratidis

Appl. Sci. 2023, 13(6), 3410; https://doi.org/10.3390/app13063410 - 7 Mar 2023

Cited by 19 | Viewed by 5525

Abstract

The cyberspace depicts an increasing number of difficulties related to security, especially in healthcare. This is evident from how vulnerable critical infrastructures are to cyberattacks and are unprotected against cybercrime. Users, ideally, should maintain a good level of cyber hygiene, via regular software updates and the development of unique passwords, as an effective way to become resilient to cyberattacks. Cyber security breaches are a top priority, and most users are aware that their behaviours may put them at risk; however, they are not educated to follow best practices, such as protecting their passwords. Mass cyber education may serve as a means to offset poor cyber security behaviours; however, mandatory education becomes a questionable point if the content is not focused on human factors, using human-centric approaches and taking into account end users’ behaviours, which is currently the case. The nature of the present paper is largely exploratory, and the purpose is two-fold: To present and explore the cyber hygiene definition, context and habits of end users in order to strengthen our understanding of users. Our paper reports the best practices that should be used by healthcare organisations and healthcare professionals to maintain good cyber hygiene and how these can be applied via a healthcare use case scenario to increase awareness related to data privacy and cybersecurity. This is an issue of great importance and urgency considering the rapid increase of cyberattacks in healthcare organisations, mainly due to human errors. Further to that, based on human-centric approaches, our long-term vision and future work involves facilitating the development of efficient practices and education associated with cybersecurity hygiene via a flexible, adaptable and practical framework. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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22 pages, 2971 KiB

Open AccessReview

Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery

by Sahalu Balarabe Junaid, Abdullahi Abubakar Imam, Muhammad Abdulkarim, Yusuf Alhaji Surakat, Abdullateef Oluwagbemiga Balogun, Ganesh Kumar, Aliyu Nuhu Shuaibu, Aliyu Garba, Yusra Sahalu, Abdullahi Mohammed, Tanko Yahaya Mohammed, Bashir Abubakar Abdulkadir, Abdallah Alkali Abba, Nana Aliyu Iliyasu Kakumi and Ahmad Sobri Hashim

Appl. Sci. 2022, 12(20), 10271; https://doi.org/10.3390/app122010271 - 12 Oct 2022

Cited by 16 | Viewed by 4928

Abstract

Artificial intelligence (AI) and wearable sensors are gradually transforming healthcare service delivery from the traditional hospital-centred model to the personal-portable-device-centred model. Studies have revealed that this transformation can provide an intelligent framework with automated solutions for clinicians to assess patients’ general health. Often, electronic systems are used to record numerous clinical records from patients. Vital sign data, which are critical clinical records are important traditional bioindicators for assessing a patient’s general physical health status and the degree of derangement happening from the baseline of the patient. The vital signs include blood pressure, body temperature, respiratory rate, and heart pulse rate. Knowing vital signs is the first critical step for any clinical evaluation, they also give clues to possible diseases and show progress towards illness recovery or deterioration. Techniques in machine learning (ML), a subfield of artificial intelligence (AI), have recently demonstrated an ability to improve analytical procedures when applied to clinical records and provide better evidence supporting clinical decisions. This literature review focuses on how researchers are exploring several benefits of embracing AI techniques and wearable sensors in tasks related to modernizing and optimizing healthcare data analyses. Likewise, challenges concerning issues associated with the use of ML and sensors in healthcare data analyses are also discussed. This review consequently highlights open research gaps and opportunities found in the literature for future studies. Full article

(This article belongs to the Special Issue eHealth Innovative Approaches and Applications)

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