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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (1 March 2024) | Viewed by 52251

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Dr. Fabio Mendonca

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Campus Universitário da Penteada, University of Madeira and ITI/Larsys, 9020-105 Funchal, Portugal
Interests: signal processing; sleep analysis; machine learning; biomedical analysis
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Dr. Morgado Dias

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Madeira Interactive Technologies Institute and ITI/Larsys, Universidade da Madeira, 9000-390 Funchal, Portugal
Interests: artificial neural networks; artificial intelligence; machine learning
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Dr. Sheikh Shanawaz Mostafa

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ITI–Interactive Technologies Institute/Larsys, 9000 Funchal, Portugal
Interests: artificial intelligence; sleep; signal and image processing
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Special Issue Information

Dear Colleagues,

In the twenty-first century, health and well-being are some of the most sought-after necessities. As society advances toward more advanced technologies, we are observing a rapid medical science improvement. Modern science not only improves knowledge but also creates different tools and systems to diagnose, treat, and investigate different diseases.

This Special Issue focuses on applications of artificial intelligence in health and medicine, covering a broad range of research topics, including monitoring, disease detection, decision making, and health assessment.

We would like to evaluate innovative research cases where artificial intelligence has brought novel views to health and medicine. Likewise, we would like to study the impact of machine learning and novel methods based on deep learning with big data on conventional and new research issues, studying how these new techniques can help improve the state of the art.

We hope this Special Issue can simultaneously bring new views on the established research topics in health and medicine while paving the ground for the newly emerging topics in these fields.

Dr. Fabio Mendonca
Dr. Morgado Dias
Dr. Sheikh Shanawaz Mostafa
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. 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.

Keywords

artificial intelligence
automatic disease classification
big data
clinical decision support
drug discovery
health promotion
health risk assessment
imaging analytics and diagnostics
machine learning and deep learning
public health

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Published Papers (11 papers)

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17 pages, 7785 KiB

Open AccessArticle

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

by Oleg E. Karpov, Elena N. Pitsik, Semen A. Kurkin, Vladimir A. Maksimenko, Alexander V. Gusev, Natali N. Shusharina and Alexander E. Hramov

Int. J. Environ. Res. Public Health 2023, 20(7), 5335; https://doi.org/10.3390/ijerph20075335 - 30 Mar 2023

Cited by 32 | Viewed by 4461

Abstract

Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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14 pages, 699 KiB

Open AccessArticle

Major Determinants of Innovation Performance in the Context of Healthcare Sector

by Abdelmohsen A. Nassani, Asad Javed, Joanna Rosak-Szyrocka, Ladislav Pilar, Zahid Yousaf and Mohamed Haffar

Int. J. Environ. Res. Public Health 2023, 20(6), 5007; https://doi.org/10.3390/ijerph20065007 - 12 Mar 2023

Cited by 6 | Viewed by 2465

Abstract

Through the innovation network (IN) and the use of artificial intelligence (AI), this study aims to look into the innovation performance (IP) of the healthcare industry. Digital innovation (DI) is also tested as a mediator. For the collection of data, cross-sectional methods and quantitative research designs were used. To test the study hypotheses, the SEM technique and multiple regression technique were used. Results reveal that AI and the innovation network support the attainment of innovation performance. The finding demonstrates that the relationship between INs and IP links and AI adoption and IP links is mediated through DI. The healthcare industry plays a vital role in facilitating public health and improving the living standards of the people. This sector’s growth and development are largely dependent on its innovativeness. This study highlights the major determinants of IP in the healthcare industry in terms of IN and AI adoption. This study adds to the literature’s knowledge via an innovative proposal in which the mediation role of DI among IN-IP and AI adoption-innovation links is investigated. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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22 pages, 1556 KiB

Open AccessArticle

Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method

by Chih-Chou Chiu, Chung-Min Wu, Te-Nien Chien, Ling-Jing Kao, Chengcheng Li and Chuan-Mei Chu

Int. J. Environ. Res. Public Health 2023, 20(5), 4340; https://doi.org/10.3390/ijerph20054340 - 28 Feb 2023

Cited by 13 | Viewed by 3767

Abstract

An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient’s age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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20 pages, 2924 KiB

Open AccessArticle

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia

by Sunday O. Olatunji, Nawal Alsheikh, Lujain Alnajrani, Alhatoon Alanazy, Meshael Almusairii, Salam Alshammasi, Aisha Alansari, Rim Zaghdoud, Alaa Alahmadi, Mohammed Imran Basheer Ahmed, Mohammed Salih Ahmed and Jamal Alhiyafi

Int. J. Environ. Res. Public Health 2023, 20(5), 4261; https://doi.org/10.3390/ijerph20054261 - 27 Feb 2023

Cited by 16 | Viewed by 3950

Abstract

Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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15 pages, 4540 KiB

Open AccessArticle

Clinical Decision Support Systems to Predict Drug–Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder

by Fadwa Alrowais, Saud S. Alotaibi, Anwer Mustafa Hilal, Radwa Marzouk, Heba Mohsen, Azza Elneil Osman, Amani A. Alneil and Mohamed I. Eldesouki

Int. J. Environ. Res. Public Health 2023, 20(3), 2696; https://doi.org/10.3390/ijerph20032696 - 2 Feb 2023

Cited by 2 | Viewed by 2000

Abstract

Big Data analytics is a technique for researching huge and varied datasets and it is designed to uncover hidden patterns, trends, and correlations, and therefore, it can be applied for making superior decisions in healthcare. Drug–drug interactions (DDIs) are a main concern in drug discovery. The main role of precise forecasting of DDIs is to increase safety potential, particularly, in drug research when multiple drugs are co-prescribed. Prevailing conventional method machine learning (ML) approaches mainly depend on handcraft features and lack generalization. Today, deep learning (DL) techniques that automatically study drug features from drug-related networks or molecular graphs have enhanced the capability of computing approaches for forecasting unknown DDIs. Therefore, in this study, we develop a sparrow search optimization with deep learning-based DDI prediction (SSODL-DDIP) technique for healthcare decision making in big data environments. The presented SSODL-DDIP technique identifies the relationship and properties of the drugs from various sources to make predictions. In addition, a multilabel long short-term memory with an autoencoder (MLSTM-AE) model is employed for the DDI prediction process. Moreover, a lexicon-based approach is involved in determining the severity of interactions among the DDIs. To improve the prediction outcomes of the MLSTM-AE model, the SSO algorithm is adopted in this work. To assure better performance of the SSODL-DDIP technique, a wide range of simulations are performed. The experimental results show the promising performance of the SSODL-DDIP technique over recent state-of-the-art algorithms. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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24 pages, 1705 KiB

Open AccessArticle

The Use of a Technology Acceptance Model (TAM) to Predict Patients’ Usage of a Personal Health Record System: The Role of Security, Privacy, and Usability

by Adi Alsyouf, Abdalwali Lutfi, Nizar Alsubahi, Fahad Nasser Alhazmi, Khalid Al-Mugheed, Rami J. Anshasi, Nora Ibrahim Alharbi and Moteb Albugami

Int. J. Environ. Res. Public Health 2023, 20(2), 1347; https://doi.org/10.3390/ijerph20021347 - 11 Jan 2023

Cited by 96 | Viewed by 12769

Abstract

Personal health records (PHR) systems are designed to ensure that individuals have access and control over their health information and to support them in being active participants rather than passive ones in their healthcare process. Yet, PHR systems have not yet been widely adopted or used by consumers despite their benefits. For these advantages to be realized, adoption of the system is necessary. In this study, we examined how self-determination of health management influences individuals’ intention to implement a PHR system, i.e., their ability to actively manage their health. Using an extended technology acceptance model (TAM), the researchers developed and empirically tested a model explaining public adoption of PHRs. In total, 389 Saudi Arabian respondents were surveyed in a quantitative cross-sectional design. The hypotheses were analysed using structural equation modelling–partial least squares (SEM-PLS4). Results indicate that PHR system usage was influenced by three major factors: perceived ease of use (PEOU), perceived usefulness (PU), and security towards intention to use. PHR PEOU and PHR intention to use were also found to be moderated by privacy, whereas usability positively moderated PHR PEOU and PHR intention to use and negatively moderated PHR PU and PHR intention to use. For the first time, this study examined the use of personal health records in Saudi Arabia, including the extension of the TAM model as well as development of a context-driven model that examines the relationship between privacy, security, usability, and the use of PHRs. Furthermore, this study fills a gap in the literature regarding the moderating effects of privacy influence on PEOU and intention to use. Further, the moderating effects of usability on the relationship between PEOU, PU, and intention to use. Study findings are expected to assist government agencies, health policymakers, and health organizations around the world, including Saudi Arabia, in understanding the adoption of personal health records. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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14 pages, 6566 KiB

Open AccessArticle

Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images

by Ahatsham Hayat, Preety Baglat, Fábio Mendonça, Sheikh Shanawaz Mostafa and Fernando Morgado-Dias

Int. J. Environ. Res. Public Health 2023, 20(2), 1268; https://doi.org/10.3390/ijerph20021268 - 10 Jan 2023

Cited by 13 | Viewed by 2844

Abstract

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people’s health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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25 pages, 738 KiB

Open AccessArticle

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models

by Aishwariya Dutta, Md. Kamrul Hasan, Mohiuddin Ahmad, Md. Abdul Awal, Md. Akhtarul Islam, Mehedi Masud and Hossam Meshref

Int. J. Environ. Res. Public Health 2022, 19(19), 12378; https://doi.org/10.3390/ijerph191912378 - 28 Sep 2022

Cited by 81 | Viewed by 7699

Abstract

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of

0.735

and an area under the ROC curve (AUC) of

0.832

. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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24 pages, 4043 KiB

Open AccessArticle

Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG

by Fábio Mendonça, Sheikh Shanawaz Mostafa, Diogo Freitas, Fernando Morgado-Dias and Antonio G. Ravelo-García

Int. J. Environ. Res. Public Health 2022, 19(17), 10892; https://doi.org/10.3390/ijerph191710892 - 1 Sep 2022

Cited by 5 | Viewed by 2246

Abstract

The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels’ feature level fusion was proposed in this work and employed for the CAP A phase classification. Two optimization algorithms optimized the channel selection, fusion, and classification procedures. The developed methodologies were evaluated by fusing the information from multiple EEG channels for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels (Fp2–F4, C4–A1, F4–C4), which is in line with the CAP protocol to ensure multiple channels’ arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being thus suitable for real-world application. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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17 pages, 2342 KiB

Open AccessArticle

Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks

by Serajeddin Ebrahimian, Ali Nahvi, Masoumeh Tashakori, Hamed Salmanzadeh, Omid Mohseni and Timo Leppänen

Int. J. Environ. Res. Public Health 2022, 19(17), 10736; https://doi.org/10.3390/ijerph191710736 - 29 Aug 2022

Cited by 22 | Viewed by 4083

Abstract

The high number of fatal crashes caused by driver drowsiness highlights the need for developing reliable drowsiness detection methods. An ideal driver drowsiness detection system should estimate multiple levels of drowsiness accurately without intervening in the driving task. This paper proposes a multi-level drowsiness detection system by a deep neural network-based classification system using a combination of electrocardiogram and respiration signals. The proposed method is based on a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for classifying drowsiness by concurrently using heart rate variability (HRV), power spectral density of HRV, and respiration rate signal as inputs. Two models, a CNN-based model and a hybrid CNN-LSTM-based model were used for multi-level classifications. The performance of the proposed method was evaluated on experimental data collected from 30 subjects in a simulated driving environment. The performance and the results of both models are presented and compared. The best performance for both three-level and five-level drowsiness classifications was achieved by the CNN-LSTM model. The results indicate that the three-level and five-level classifications of drowsiness can be achieved with 91 and 67% accuracy, respectively. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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Review

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15 pages, 883 KiB

Open AccessReview

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

by Iuliu Alexandru Pap and Stefan Oniga

Int. J. Environ. Res. Public Health 2022, 19(18), 11413; https://doi.org/10.3390/ijerph191811413 - 10 Sep 2022

Cited by 14 | Viewed by 4209

Abstract

Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence to Health)

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