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Information Technologies Applied on Healthcare

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A special issue of Healthcare (ISSN 2227-9032).

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 52335

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Special Issue Editors

Prof. Dr. Giner Alor-Hernández

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Guest Editor

Tecnológico Nacional de México/ I. T. Orizaba, Orizaba 94320, Mexico
Interests: big data; Internet of Things; knowledge management; software engineering; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Jezreel Mejía-Miranda

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Guest Editor

Centro de Investigación en Matemáticas (CIMAT), Zacatecas 98160, Mexico
Interests: software engineering; quality; IT security; software process improvement
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Prof. Dr. José Luis Sánchez-Cervantes

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Tecnológico Nacional de México/ I. T. Orizaba, Orizaba 94320, Mexico
Interests: artificial intelligence; knowledge acquisition; semantic web; linked open data; big data
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Prof. Dr. Alejandro Rodríguez-González

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Guest Editor

Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: medical informatics; artificial intelligence; disease networks
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Special Issue Information

Dear Colleagues,

The Healthcare has been increased its importance in the last years, and it will be central to recovery in several sectors to the global level such as economy, education, tourism and the commerce, to mention but a few. Although the vaccinating the world against Covid-19 will remain a core priority in 2022, according to analysis carried out by international organism as the UNICEF and European Union, by the year 2030, it is expected a huge increment in the use of e-health services from young people where emergent technologies including the Artificial Intelligence, Big Data, the Web, the IoT Technologies and mobile devices, as well governmental policies, will play a crucial role in the success of delivery of relevant data for health professionals and to get information and advice in benefit of health consumers.

According to these premises, research papers, short communications, perspective article, and reviews are all welcome at this special issue. This special issue has the purpose of collecting and consolidating innovative and high-quality research contributions regarding to Information Technologies Applied on Healthcare to different disciplines and its challenges such as the systematizing and standardization of healthcare information systems, detection of diseases at early stage, open healthcare data, integrated health services, cybersecurity & data protection in Healthcare, interoperability data health, among others.

Prof. Dr. Giner Alor-Hernández
Dr. Jezreel Mejía-Miranda
Prof. Dr. José Luis Sánchez-Cervantes
Prof. Dr. Alejandro Rodríguez-González
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. Healthcare 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 2700 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

information Technologies in Healthcare
Human-Computer Interaction (HCI) in Healthcare
intelligent medical devices and smart technologies
artificial intelligent techniques applied to healthcare
digital healthcare
telehealth (telemonitoring for diseases, remote consultation, remote education and support)
prognosis, diagnosis and treatment in healthcare
big data analytics for healthcare
computer games for healthcare
m-Health
smart technologies for healthcare
predictive modelling and analytics for healthcare
computer vision in healthcare
healthcare decision support systems

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Published Papers (10 papers)

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Research

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24 pages, 317 KiB

Open AccessArticle

Identifying the Barriers to Acceptance of Blockchain-Based Patient-Centric Data Management Systems in Healthcare

by Ibrahim Mutambik, John Lee, Abdullah Almuqrin and Zahyah H. Alharbi

Healthcare 2024, 12(3), 345; https://doi.org/10.3390/healthcare12030345 - 30 Jan 2024

Cited by 7 | Viewed by 1429

Abstract

A number of recent studies have shown that wastage and inefficiency are a significant problem in all global healthcare systems. One initiative that could radically improve the operational efficiency of health systems is to make a paradigm shift in data ownership—that is, to transition such systems to a patient-centric model of data management by deploying blockchain technology. Such a development would not only make an economic impact, by radically cutting wastage, but would deliver significant social benefits by improving patient outcomes and satisfaction. However, a blockchain-based solution presents considerable challenges. This research seeks to understand the principal factors, which act as barriers to the acceptance of a blockchain-based patient-centric data management infrastructure, in the healthcare systems of the GCC (Gulf Cooperation Council) countries. The study represents an addition to the current literature by examining the perspectives and views of healthcare professionals and users. This approach is rare within this subject area, and is identified in existing systematic reviews as a research gap: a qualitative investigation of motivations and attitudes among these groups is a critical need. The results of the study identified 12 key barriers to the acceptance of blockchain infrastructures, thereby adding to our understanding of the challenges that need to be overcome in order to benefit from this relatively recent technology. The research is expected to be of use to healthcare authorities in planning a way forward for system improvement, particularly in terms of successfully introducing patient-centric systems. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

16 pages, 8288 KiB

Open AccessArticle

Accurate Prediction of Anxiety Levels in Asian Countries Using a Fuzzy Expert System

by Mouz Ramzan, Muhammad Hamid, Amel Ali Alhussan, Hussah Nasser AlEisa and Hanaa A. Abdallah

Healthcare 2023, 11(11), 1594; https://doi.org/10.3390/healthcare11111594 - 30 May 2023

Cited by 3 | Viewed by 1676

Abstract

Anxiety is a common mental health issue that affects a significant portion of the global population and can lead to severe physical and psychological consequences. The proposed system aims to provide an objective and reliable method for the early detection of anxiety levels by using patients’ physical symptoms as input variables. This paper introduces an expert system utilizing a fuzzy inference system (FIS) to predict anxiety levels. The system is designed to address anxiety’s complex and uncertain nature by utilizing a comprehensive set of input variables and fuzzy logic techniques. It is based on a set of rules that represent medical knowledge of anxiety disorders, making it a valuable tool for clinicians in diagnosing and treating these disorders. The system was tested on real datasets, demonstrating high accuracy in the prediction of anxiety levels. The FIS-based expert system offers a powerful approach to cope with imprecision and uncertainty and can potentially assist in addressing the lack of effective remedies for anxiety disorders. The research primarily focused on Asian countries, such as Pakistan, and the system achieved an accuracy of 87%, which is noteworthy. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

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23 pages, 9244 KiB

Open AccessArticle

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

by Agne Paulauskaite-Taraseviciene, Julius Siaulys, Kristina Sutiene, Titas Petravicius, Skirmantas Navickas, Marius Oliandra, Andrius Rapalis and Justinas Balciunas

Healthcare 2023, 11(8), 1152; https://doi.org/10.3390/healthcare11081152 - 17 Apr 2023

Cited by 7 | Viewed by 3285

Abstract

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients’ data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient’s position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

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18 pages, 2712 KiB

Open AccessArticle

Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation

by Jorge Pool-Cen, Hugo Carlos-Martínez, Gandhi Hernández-Chan and Oscar Sánchez-Siordia

Healthcare 2023, 11(7), 1057; https://doi.org/10.3390/healthcare11071057 - 6 Apr 2023

Cited by 2 | Viewed by 2371

Abstract

Mental health problems are one of the various ills that afflict the world’s population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology’s effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

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20 pages, 1346 KiB

Open AccessArticle

Are Health Information Systems Ready for the Digital Transformation in Portugal? Challenges and Future Perspectives

by Leonor Teixeira, Irene Cardoso, Jorge Oliveira e Sá and Filipe Madeira

Healthcare 2023, 11(5), 712; https://doi.org/10.3390/healthcare11050712 - 28 Feb 2023

Cited by 6 | Viewed by 3259

Abstract

Purpose: This study aimed to reflect on the challenges of Health Information Systems in Portugal at a time when technologies enable the creation of new approaches and models for care provision, as well as to identify scenarios that may characterize this practice in the future. Design/methodology/approach: A guiding research model was created based on an empirical study that was conducted using a qualitative method that integrated content analysis of strategic documents and semi-structured interviews with a sample of fourteen key actors in the health sector. Findings: Results pointed to the existence of emerging technologies that may promote the development of Health Information Systems oriented to “health and well-being” in a preventive model logic and reinforce the social and management implications. Originality/value: The originality of this work resided in the empirical study carried out, which allowed us to analyze how the various actors look at the present and the future of Health Information Systems. There is also a lack of studies addressing this subject. Research limitations/implications: The main limitations resulted from a low, although representative, number of interviews and the fact that the interviews took place before the pandemic, so the digital transformation that was promoted was not reflected. Managerial implications and social implications: The study highlighted the need for greater commitment from decision makers, managers, healthcare providers, and citizens toward achieving improved digital literacy and health. Decision makers and managers must also agree on strategies to accelerate existing strategic plans and avoid their implementation at different paces. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

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19 pages, 4975 KiB

Open AccessArticle

Deep Learning-Based Yoga Posture Recognition Using the Y_PN-MSSD Model for Yoga Practitioners

by Aman Upadhyay, Niha Kamal Basha and Balasundaram Ananthakrishnan

Healthcare 2023, 11(4), 609; https://doi.org/10.3390/healthcare11040609 - 17 Feb 2023

Cited by 21 | Viewed by 6169

Abstract

In today’s digital world, and in light of the growing pandemic, many yoga instructors opt to teach online. However, even after learning or being trained by the best sources available, such as videos, blogs, journals, or essays, there is no live tracking available to the user to see if he or she is holding poses appropriately, which can lead to body posture issues and health issues later in life. Existing technology can assist in this regard; however, beginner-level yoga practitioners have no means of knowing whether their position is good or poor without the instructor’s help. As a result, the automatic assessment of yoga postures is proposed for yoga posture recognition, which can alert practitioners by using the Y_PN-MSSD model, in which Pose-Net and Mobile-Net SSD (together named as TFlite Movenet) play a major role. The Pose-Net layer takes care of the feature point detection, while the mobile-net SSD layer performs human detection in each frame. The model is categorized into three stages. Initially, there is the data collection/preparation stage, where the yoga postures are captured from four users as well as an open-source dataset with seven yoga poses. Then, by using these collected data, the model undergoes training where the feature extraction takes place by connecting key points of the human body. Finally, the yoga posture is recognized and the model assists the user through yoga poses by live-tracking them, as well as correcting them on the fly with 99.88% accuracy. Comparatively, this model outperforms the performance of the Pose-Net CNN model. As a result, the model can be used as a starting point for creating a system that will help humans practice yoga with the help of a clever, inexpensive, and impressive virtual yoga trainer. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

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18 pages, 1606 KiB

Open AccessArticle

A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan

by Chien-Ta Ho and Cheng-Yi Wang

Healthcare 2022, 10(9), 1759; https://doi.org/10.3390/healthcare10091759 - 13 Sep 2022

Cited by 1 | Viewed by 2012

Abstract

Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

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16 pages, 1089 KiB

Open AccessArticle

Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things

by Mohamed E. Issa, Ahmed M. Helmi, Mohammed A. A. Al-Qaness, Abdelghani Dahou, Mohamed Abd Elaziz and Robertas Damaševičius

Healthcare 2022, 10(6), 1084; https://doi.org/10.3390/healthcare10061084 - 10 Jun 2022

Cited by 30 | Viewed by 4385

Abstract

Nowadays, the emerging information technologies in smart handheld devices are motivating the research community to make use of embedded sensors in such devices for healthcare purposes. In particular, inertial measurement sensors such as accelerometers and gyroscopes embedded in smartphones and smartwatches can provide sensory data fusion for human activities and gestures. Thus, the concepts of the Internet of Healthcare Things (IoHT) paradigm can be applied to handle such sensory data and maximize the benefits of collecting and analyzing them. The application areas contain but are not restricted to the rehabilitation of elderly people, fall detection, smoking control, sportive exercises, and monitoring of daily life activities. In this work, a public dataset collected using two smartphones (in pocket and wrist positions) is considered for IoHT applications. Three-dimensional inertia signals of thirteen timestamped human activities such as Walking, Walking Upstairs, Walking Downstairs, Writing, Smoking, and others are registered. Here, an efficient human activity recognition (HAR) model is presented based on efficient handcrafted features and Random Forest as a classifier. Simulation results ensure the superiority of the applied model over others introduced in the literature for the same dataset. Moreover, different approaches to evaluating such models are considered, as well as implementation issues. The accuracy of the current model reaches 98.7% on average. The current model performance is also verified using the WISDM v1 dataset. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

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Review

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24 pages, 12540 KiB

Open AccessReview

Internet-Based Healthcare Knowledge Service for Improvement of Chinese Medicine Healthcare Service Quality

by Xiaoyu Wang, Yi Xie, Xuejie Yang and Dongxiao Gu

Healthcare 2023, 11(15), 2170; https://doi.org/10.3390/healthcare11152170 - 31 Jul 2023

Cited by 1 | Viewed by 2083

Abstract

With the development of new-generation information technology and increasing health needs, the requirements for Chinese medicine (CM) services have shifted toward the 5P medical mode, which emphasizes preventive, predictive, personalized, participatory, and precision medicine. This implies that CM knowledge services need to be smarter and more sophisticated. This study adopted a bibliometric approach to investigate the current state of development of CM knowledge services, and points out that accurate knowledge service is an inevitable requirement for the modernization of CM. We summarized the concept of smart CM knowledge services and highlighted its main features, including medical homogeneity, knowledge service intelligence, integration of education and research, and precision medicine. Additionally, we explored the intelligent service method of traditional Chinese medicine under the 5P medical mode to support CM automatic knowledge organization and safe sharing, human–machine collaborative knowledge discovery and personalized dynamic knowledge recommendation. Finally, we summarized the innovative modes of CM knowledge services. Our research will guide the quality assurance and innovative development of the traditional Chinese medicine knowledge service model in the era of digital intelligence. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

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13 pages, 1175 KiB

Open AccessReview

A Systematic Literature Review of Health Information Systems for Healthcare

by Ayogeboh Epizitone, Smangele Pretty Moyane and Israel Edem Agbehadji

Healthcare 2023, 11(7), 959; https://doi.org/10.3390/healthcare11070959 - 27 Mar 2023

Cited by 16 | Viewed by 23106

Abstract

Health information system deployment has been driven by the transformation and digitalization currently confronting healthcare. The need and potential of these systems within healthcare have been tremendously driven by the global instability that has affected several interrelated sectors. Accordingly, many research studies have reported on the inadequacies of these systems within the healthcare arena, which have distorted their potential and offerings to revolutionize healthcare. Thus, through a comprehensive review of the extant literature, this study presents a critique of the health information system for healthcare to supplement the gap created as a result of the lack of an in-depth outlook of the current health information system from a holistic slant. From the studies, the health information system was ascertained to be crucial and fundament in the drive of information and knowledge management for healthcare. Additionally, it was asserted to have transformed and shaped healthcare from its conception despite its flaws. Moreover, research has envisioned that the appraisal of the current health information system would influence its adoption and solidify its enactment within the global healthcare space, which is highly demanded. Full article

(This article belongs to the Special Issue Information Technologies Applied on Healthcare)

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