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A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Medicine".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 31228

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Special Issue Editors

Prof. Dr. Alejandro Rodríguez-González

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Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: medical informatics; artificial intelligence; disease networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. KC Santosh

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Applied AI Research Lab, Department of Computer Science, The University of South Dakota, Vermillion, SD 57069, USA
Interests: AI; machine learning; computer vision; pattern recognition; biomedical imaging; healthcare informatics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Linlin Shen

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School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Interests: medical image analysis; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Rosa Sicilia

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Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Via Alvaro del Portillo, 21, 00128 Rome, Italy
Interests: artificial intelligence; radiomics and radiogenomics; decision support systems

Dr. João Rafael Almeida

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Departamento de Electrónica, Telecomunicações e Informática, DETI, Universidade de Aveiro, Aveiro, Portugal
Interests: medical informatics; data privacy; data interoperability

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your work to the Special Issue on Computer-Based Medical Systems in Healthcare journal. The creation of the first Information and Communication Technologies (ICT) implied a dramatic change at a worldwide level. The adoption of those technologies has tremendously changed current society, especially with the development of the latest computer technologies and the Internet. This has affected many levels, but healthcare is one of the areas where the use of ICT has provided an incredible change over time, and it is still providing it. From the development of the first medical devices to current software and large data infrastructures, through the application of Artificial Intelligence-based approaches, medicine has dramatically changed with the application of Computer-Based Medical Systems.

This special issue aims to attract last developments on applications of ICT to healthcare and medicine. The special issue has been organized along with the 35th IEEE Computer-Based Medical Systems (CBMS) conference and aims to attract submissions both from extended works published in the conference, as well as from other potential researchers that have not been part of the conference.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

Biomedical Signal and Image Processing
Clinical and Healthcare Services Research
Data Analysis and Visualization
Data Mining and Machine Learning
Decision Support and Recommendation Systems
Healthcare Communication Networks
Healthcare Data and Knowledge Management
Human-Computer Interaction (HCI) in Healthcare
Information Technologies in Healthcare
Intelligent Medical Devices and Smart Technologies
Radiomics and Radiogenomics
Semantics and Knowledge Representation
Serious Gaming in Healthcare
Systems Integration and Security
Technology-enabled Education
Telemedicine Systems
Translational Bioinformatics

We look forward to receiving your contributions.

Prof. Dr. Alejandro Rodríguez-González
Prof. Dr. KC Santosh
Prof. Dr. Linlin Shen
Dr. Rosa Sicilia
Dr. João Rafael Almeida
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

medical systems
computers
medical devices
artificial intelligence
ICT
social

Published Papers (9 papers)

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Research

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0 pages, 1868 KiB

Open AccessArticle

Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP

by Debasmita GhoshRoy, Parvez Ahmad Alvi and KC Santosh

Healthcare 2023, 11(7), 929; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare11070929 - 23 Mar 2023

Cited by 5 | Viewed by 1885

Abstract

Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in lifestyle and environmental factors are the prime reasons for the declining rate of male fertility. Artificial intelligence (AI)/machine learning (ML) models have become an effective solution for early fertility detection. Seven industry-standard ML models are used: support vector machine, random forest (RF), decision tree, logistic regression, naïve bayes, adaboost, and multi-layer perception to detect male fertility. Shapley additive explanations (SHAP) are vital tools that examine the feature’s impact on each model’s decision making. On these, we perform a comprehensive comparative study to identify good and poor classification models. While dealing with the all-above-mentioned models, the RF model achieves an optimal accuracy and area under curve (AUC) of 90.47% and 99.98%, respectively, by considering five-fold cross-validation (CV) with the balanced dataset. Furthermore, we provide the SHAP explanations of existing models that attain good and poor performance. The findings of this study show that decision making (based on ML models) with SHAP provides thorough explanations for detecting male fertility, as well as a reference for clinicians for further treatment planning. Full article

(This article belongs to the Special Issue Computer-Based Medical Systems)

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16 pages, 2329 KiB

Open AccessArticle

Is It Possible to Predict COVID-19? Stochastic System Dynamic Model of Infection Spread in Kazakhstan

by Berik Koichubekov, Aliya Takuadina, Ilya Korshukov, Anar Turmukhambetova and Marina Sorokina

Healthcare 2023, 11(5), 752; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare11050752 - 03 Mar 2023

Cited by 1 | Viewed by 1221

Abstract

Background: Since the start of the COVID-19 pandemic, scientists have begun to actively use models to determine the epidemiological characteristics of the pathogen. The transmission rate, recovery rate and loss of immunity to the COVID-19 virus change over time and depend on many factors, such as the seasonality of pneumonia, mobility, testing frequency, the use of masks, the weather, social behavior, stress, public health measures, etc. Therefore, the aim of our study was to predict COVID-19 using a stochastic model based on the system dynamics approach. Method: We developed a modified SIR model in AnyLogic software. The key stochastic component of the model is the transmission rate, which we consider as an implementation of Gaussian random walks with unknown variance, which was learned from real data. Results: The real data of total cases turned out to be outside the predicted minimum–maximum interval. The minimum predicted values of total cases were closest to the real data. Thus, the stochastic model we propose gives satisfactory results for predicting COVID-19 from 25 to 100 days. The information we currently have about this infection does not allow us to make predictions with high accuracy in the medium and long term. Conclusions: In our opinion, the problem of the long-term forecasting of COVID-19 is associated with the absence of any educated guess regarding the dynamics of β(t) in the future. The proposed model requires improvement with the elimination of limitations and the inclusion of more stochastic parameters. Full article

(This article belongs to the Special Issue Computer-Based Medical Systems)

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25 pages, 5381 KiB

Open AccessArticle

A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia

by Dimas Chaerul Ekty Saputra, Khamron Sunat and Tri Ratnaningsih

Healthcare 2023, 11(5), 697; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare11050697 - 26 Feb 2023

Cited by 20 | Viewed by 4942

Abstract

The procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed 99.21% accuracy, 98.44% sensitivity, 99.30% precision, and an F1 score of 98.84%. Full article

(This article belongs to the Special Issue Computer-Based Medical Systems)

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26 pages, 12326 KiB

Open AccessArticle

Linked Argumentation Graphs for Multidisciplinary Decision Support

by Liang Xiao and Des Greer

Healthcare 2023, 11(4), 585; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare11040585 - 15 Feb 2023

Viewed by 1328

Abstract

Multidisciplinary clinical decision-making has become increasingly important for complex diseases, such as cancers, as medicine has become very specialized. Multiagent systems (MASs) provide a suitable framework to support multidisciplinary decisions. In the past years, a number of agent-oriented approaches have been developed on the basis of argumentation models. However, very limited work has focused, thus far, on systematic support for argumentation in communication among multiple agents spanning various decision sites and holding varying beliefs. There is a need for an appropriate argumentation scheme and identification of recurring styles or patterns of multiagent argument linking to enable versatile multidisciplinary decision applications. We propose, in this paper, a method of linked argumentation graphs and three types of patterns corresponding to scenarios of agents changing the minds of others (argumentation) and their own (belief revision): the collaboration pattern, the negotiation pattern, and the persuasion pattern. This approach is demonstrated using a case study of breast cancer and lifelong recommendations, as the survival rates of diagnosed cancer patients are rising and comorbidity is the norm. Full article

(This article belongs to the Special Issue Computer-Based Medical Systems)

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14 pages, 4329 KiB

Open AccessArticle

Diagnosis of Osteoporosis by Quantifying Volumetric Bone Mineral Density of Lumbar Vertebrae Using Abdominal CT Images and Two-Compartment Model

by Po-Chieh Hsu, Dmytro Luzhbin, Tia-Yu Shih and Jay Wu

Healthcare 2023, 11(4), 556; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare11040556 - 13 Feb 2023

Viewed by 2899

Abstract

With the aging population, osteoporosis has become an important public health issue. The purpose of this study was to establish a two-compartment model (TCM) to quantify the volumetric bone mineral density (vBMD) of the lumbar spine using abdominal computed tomography (CT) images. The TCM approach uses water as the bone marrow equivalent and K₂HPO₄ solution as the cortical bone equivalent. A phantom study was performed to evaluate the accuracy of vBMD estimation at 100 kVp and 120 kVp. The data of 180 patients who underwent abdominal CT imaging and dual-energy X-ray absorptiometry (DXA) within one month were retrospectively collected. vBMD of L1–L4 vertebrae were calculated, and the receiver-operating characteristic curve analysis was performed to establish the diagnostic thresholds for osteoporosis and osteopenia in terms of vBMD. The average difference between the measured vBMD following TCM and the theoretical vBMD of the self-made phantom was 0.2%, and the maximum difference was 0.5%. vBMD of lumbar vertebrae obtained from TCM and aBMD obtained by DXA had a significant positive correlation (r = 0.655 to 0.723). The average diagnostic threshold for osteoporosis was 0.116 g/cm³. The sensitivity, specificity, and accuracy were 95.7%, 75.6.5%, and 80.0%, respectively. The average diagnostic threshold for osteopenia was 0.126 g/cm³. The sensitivity, specificity, and accuracy were 81.3%, 82.5%, and 82.7%, respectively. The aforementioned threshold values were used to perform the diagnostics on a test cohort, and the performance was equivalent to that in the experimental cohort. From the perspective of preventive medicine, opportunistic screening of bone mineral density using abdominal CT images and the TCM approach can facilitate early detection of osteoporosis and osteopenia and, with in-time treatment, slow down their progression. Full article

(This article belongs to the Special Issue Computer-Based Medical Systems)

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16 pages, 1962 KiB

Open AccessArticle

A GDPR-Compliant Dynamic Consent Mobile Application for the Australasian Type-1 Diabetes Data Network

by Zhe Wang, Anthony Stell, Richard O. Sinnott and the ADDN Study Group

Healthcare 2023, 11(4), 496; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare11040496 - 08 Feb 2023

Viewed by 1394

Abstract

Australia has a high prevalence of diabetes, with approximately 1.2 million Australians diagnosed with the disease. In 2012, the Australasian Diabetes Data Network (ADDN) was established with funding from the Juvenile Diabetes Research Foundation (JDRF). ADDN is a national diabetes registry which captures longitudinal information about patients with type-1 diabetes (T1D). Currently, the ADDN data are directly contributed from 42 paediatric and 17 adult diabetes centres across Australia and New Zealand, i.e., where the data are pre-existing in hospital systems and not manually entered into ADDN. The historical data in ADDN have been de-identified, and patients are initially afforded the opportunity to opt-out of being involved in the registry; however, moving forward, there is an increased demand from the clinical research community to utilise fully identifying data. This raises additional demands on the registry in terms of security, privacy, and the nature of patient consent. General Data Protection Regulation (GDPR) is an increasingly important mechanism allowing individuals to have the right to know about their health data and what those data are being used for. This paper presents a mobile application being designed to support the ADDN data collection and usage processes and aligning them with GDPR. The app utilises Dynamic Consent—an informed specific consent model, which allows participants to view and modify their research-driven consent decisions through an interactive interface. It focuses specifically on supporting dynamic opt-in consent to both the registry and to associated sub-projects requesting access to and use of the patient data for research purposes. Full article

(This article belongs to the Special Issue Computer-Based Medical Systems)

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17 pages, 2687 KiB

Open AccessArticle

Semantic Data Visualisation for Biomedical Database Catalogues

by Arnaldo Pereira, João Rafael Almeida, Rui Pedro Lopes and José Luís Oliveira

Healthcare 2022, 10(11), 2287; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare10112287 - 15 Nov 2022

Viewed by 1700

Abstract

Biomedical databases often have restricted access policies and governance rules. Thus, an adequate description of their content is essential for researchers who wish to use them for medical research. A strategy for publishing information without disclosing patient-level data is through database fingerprinting and aggregate characterisations. However, this information is still presented in a format that makes it challenging to search, analyse, and decide on the best databases for a domain of study. Several strategies allow one to visualise and compare the characteristics of multiple biomedical databases. Our study focused on a European platform for sharing and disseminating biomedical data. We use semantic data visualisation techniques to assist in comparing descriptive metadata from several databases. The great advantage lies in streamlining the database selection process, ensuring that sensitive details are not shared. To address this goal, we have considered two levels of data visualisation, one characterising a single database and the other involving multiple databases in network-level visualisations. This study revealed the impact of the proposed visualisations and some open challenges in representing semantically annotated biomedical datasets. Identifying future directions in this scope was one of the outcomes of this work. Full article

(This article belongs to the Special Issue Computer-Based Medical Systems)

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Review

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13 pages, 1217 KiB

Open AccessReview

Analyzing Overlaid Foreign Objects in Chest X-rays—Clinical Significance and Artificial Intelligence Tools

by Shotabdi Roy and KC Santosh

Healthcare 2023, 11(3), 308; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare11030308 - 19 Jan 2023

Cited by 6 | Viewed by 4862

Abstract

The presence of non-biomedical foreign objects (NBFO), such as coins, buttons and jewelry, and biomedical foreign objects (BFO), such as medical tubes and devices in chest X-rays (CXRs), make accurate interpretation difficult, as they do not indicate known biological abnormalities like excess fluids, tuberculosis (TB) or cysts. Such foreign objects need to be detected, localized, categorized as either NBFO or BFO, and removed from CXR or highlighted in CXR for effective abnormality analysis. Very specifically, NBFOs can adversely impact the process, as typical machine learning algorithms would consider these objects to be biological abnormalities producing false-positive cases. It holds true for BFOs in CXRs. This paper examines detailed discussions on numerous clinical reports in addition to computer-aided detection (CADe) with diagnosis (CADx) tools, where both shallow learning and deep learning algorithms are applied. Our discussion reflects the importance of accurately detecting, isolating, classifying, and either removing or highlighting NBFOs and BFOs in CXRs by taking 29 peer-reviewed research reports and articles into account. Full article

(This article belongs to the Special Issue Computer-Based Medical Systems)

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Other

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10 pages, 297 KiB

Open AccessViewpoint

Telehealth and COVID-19 Pandemic: An Overview of the Telehealth Use, Advantages, Challenges, and Opportunities during COVID-19 Pandemic

by Khayreddine Bouabida, Bertrand Lebouché and Marie-Pascale Pomey

Healthcare 2022, 10(11), 2293; https://0-doi-org.brum.beds.ac.uk/10.3390/healthcare10112293 - 16 Nov 2022

Cited by 32 | Viewed by 8938

Abstract

The use of telehealth and digital health platforms has increased during the COVID-19 pandemic due to the implementation of physical distancing measures and restrictions. To address the pandemic threat, telehealth was promptly and extensively developed, implemented, and used to maintain continuity of care offered through multi-purpose technology platforms considered as virtual healthcare facilities. The aim of this paper is to define telehealth and discuss some aspects of its utilization, role, and impact, but also opportunities and future implications particularly during the COVID-19 pandemic. In order to support our reflection and consolidate our viewpoints, numerous bibliographical sources and relevant literature were identified through an electronic keyword search of four databases (PubMed, Web of Science, Google Scholar, and ResearchGate). In this paper, we consider that telehealth to be a very interesting approach which can be effective and affordable for health systems aiming to facilitate access to care, maintain quality and safety of care, and engage patients and health professionals and users of health services. However, we also believe that telehealth faces many challenges, such as the issue of lack of human contact in care, confidentiality, and data security, also accessibility and training in the use of platforms for telehealth. Despite the many challenges it faces, we believe telehealth has enormous potential for strengthening and improving healthcare services. In this paper, we also call for and encourage further studies to build a solid and broad understanding of telehealth challenges with its short-term and long-term clinical, organizational, socio-economic, and ethical impacts. Full article

(This article belongs to the Special Issue Computer-Based Medical Systems)

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