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Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process

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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Health Care Sciences & Services".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 43912

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Special Issue Editor

Dr. Kang Hao Cheong

E-Mail Website1 Website2
Chief Guest Editor

1. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639818, Singapore
2. Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
Interests: game theory; extended reality; data science; AI/ML in the medical field/healthcare; pedagogy and educational research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Prediction, detection, treatment, and rehabilitation are of great importance in any healthcare process. The complexity of healthcare brings with it the need to perform simulations and/or apply machine learning algorithms for prediction, detection, treatment or rehabilitation. With the rise in big data, these data can also be used to enhance the current systems of modeling or predictive analysis. Therefore, it is in the interest, as part of enhancing modern healthcare, to tap into big data using viable simulations and machine learning techniques to enhance the state of our current healthcare services.

This Special Issue offers an opportunity for novel interdisciplinary research and reviews that report on the extensive range of simulation or machine learning techniques applied to public healthcare using big data. We welcome manuscripts focusing on, but not restricted to, medical technologies, clinical practice, complexity study, simulations and validation through data fitting, machine learning, and novel methods of collecting data in healthcare. The study should cover aspects of detection, treatment or rehabilitation.

Dr. Kang Hao Cheong
Guest Editors

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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. 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

early detection
diagnosis and treatment
decision making
classification of early symptoms
end of life care
deep learning
artificial intelligence and machine learning algorithms
big data curation
medical data
medical informatics
transformational healthcare
healthcare technology
assistive technology

Published Papers (13 papers)

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Research

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10 pages, 347 KiB

Open AccessArticle

Clustering of Environmental Parameters and the Risk of Acute Ischaemic Stroke

by Geraldine P. Y. Koo, Huili Zheng, Joel C. L. Aik, Benjamin Y. Q. Tan, Vijay K. Sharma, Ching Hui Sia, Marcus E. H. Ong and Andrew F. W. Ho

Int. J. Environ. Res. Public Health 2023, 20(6), 4979; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20064979 - 11 Mar 2023

Cited by 2 | Viewed by 1620

Abstract

Acute ischaemic stroke (AIS) risk on days with similar environmental profiles remains unknown. We investigated the association between clusters of days with similar environmental parameters and AIS incidence in Singapore. We grouped calendar days from 2010 to 2015 with similar rainfall, temperature, wind speed, and Pollutant Standards Index (PSI) using k-means clustering. Three distinct clusters were formed ‘Cluster 1’ containing high wind speed, ‘Cluster 2’ having high rainfall, and ‘Cluster 3’ having high temperatures and PSI. We aggregated the number of AIS episodes over the same period with the clusters and analysed their association using a conditional Poisson regression in a time-stratified case-crossover design. Comparing the three clusters, Cluster 3 had the highest AIS occurrence (IRR 1.09; 95% confidence interval (CI) 1.05–1.13), with no significant difference between Clusters 1 and 2. Subgroup analyses in Cluster 3 showed that AIS risk was amplified in the elderly (≥65 years old), non-smokers, and those without a history of ischaemic heart disease/atrial fibrillation/vascular heart disease/peripheral vascular disease. In conclusion, we found that AIS incidence may be higher on days with higher temperatures and PSI. These findings have important public health implications for AIS prevention and health services delivery during at-risk days, such as during the seasonal transboundary haze. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

12 pages, 1688 KiB

Open AccessArticle

Ambient Air Quality and Emergency Hospital Admissions in Singapore: A Time-Series Analysis

by Andrew Fu Wah Ho, Zhongxun Hu, Ting Zhen Cheryl Woo, Kenneth Boon Kiat Tan, Jia Hao Lim, Maye Woo, Nan Liu, Geoffrey G. Morgan, Marcus Eng Hock Ong and Joel Aik

Int. J. Environ. Res. Public Health 2022, 19(20), 13336; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph192013336 - 16 Oct 2022

Cited by 3 | Viewed by 1678

Abstract

Air pollution exposure may increase the demand for emergency healthcare services, particularly in South-East Asia, where the burden of air-pollution-related health impacts is high. This article aims to investigate the association between air quality and emergency hospital admissions in Singapore. Quasi-Poisson regression was applied with a distributed lag non-linear model (DLNM) to assess the short-term associations between air quality variations and all-cause, emergency admissions from a major hospital in Singapore, between 2009 and 2017. Higher concentrations of SO₂, PM_2.5, PM₁₀, NO₂, and CO were positively associated with an increased risk of (i) all-cause, (ii) cardiovascular-related, and (iii) respiratory-related emergency admissions over 7 days. O₃ concentration increases were associated with a non-linear decrease in emergency admissions. Females experienced a higher risk of emergency admissions associated with PM_2.5, PM₁₀, and CO exposure, and a lower risk of admissions with NO₂ exposure, compared to males. The older adults (≥65 years) experienced a higher risk of emergency admissions associated with SO₂ and O₃ exposure compared to the non-elderly group. We found significant positive associations between respiratory disease- and cardiovascular disease-related emergency hospital admissions and ambient SO₂, PM_2.5, PM₁₀, NO₂, and CO concentrations. Age and gender were identified as effect modifiers of all-cause admissions. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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16 pages, 4044 KiB

Open AccessArticle

Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network

by Haijing Sun, Anna Wang and Shanshan He

Int. J. Environ. Res. Public Health 2022, 19(8), 4508; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19084508 - 08 Apr 2022

Cited by 11 | Viewed by 2680

Abstract

Most current research on Alzheimer’s disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a good measure of the progression of AD. Currently, there is no cure for patients with existing AD dementia, but patients with mild cognitive impairment (MCI) in the prodromal stage of AD dementia may be diagnosed. The study of the early diagnosis of MCI and the prediction of MCI to AD transformation is of great significance for the monitoring of the MCI to AD transformation process. Despite the high rate of MCI conversion to AD, the neuropathological cause of MCI is heterogeneous. However, many people with MCI remain stable. Treatment options are different for patients with stable MCI and those with underlying dementia. Therefore, it is of great significance for clinical practice to predict whether patients with MCI will develop AD dementia. This paper proposes an improved algorithm that is based on a convolution neural network (CNN) with residuals combined with multi-layer long short-term memory (LSTM) to diagnose AD and predict MCI. Firstly, multi-time resting-state fMRI images were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database for preprocessing, and then an AAL brain partition template was used to construct a 90 × 90 functional connectivity (FC) network matrix of a whole-brain region of interest (ROI). Secondly, the diversity of training samples was increased by generating an adversarial network (GAN). Finally, a CNN with residuals and a multi-layer LSTM model were constructed to automatically classify and predict the functional adjacency matrix. This method can not only distinguish Alzheimer’s disease from normal health conditions at multiple time points, but can also predict progressive MCI (pMCI) and stable MCI (sMCI) at multiple time points. The classification accuracies in AD vs. NC and sMCI vs.pMCI reached 93.5% and 75.5%, respectively. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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28 pages, 13442 KiB

Open AccessArticle

Electrocardiogram Fiducial Point Detector Using a Bilateral Filter and Symmetrical Point-Filter Structure

by Tae-Wuk Bae, Kee-Koo Kwon and Kyu-Hyung Kim

Int. J. Environ. Res. Public Health 2021, 18(20), 10792; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182010792 - 14 Oct 2021

Cited by 4 | Viewed by 2150

Abstract

The characteristics or aspects of important fiducial points (FPs) in the electrocardiogram (ECG) signal are complicated because of various factors, such as non-stationary effects and low signal-to-noise ratio. Due to the various noises caused by the ECG signal measurement environment and by typical ECG signal deformation due to heart diseases, detecting such FPs becomes a challenging task. In this study, we introduce a novel PQRST complex detector using a one-dimensional bilateral filter (1DBF) and the temporal characteristics of FPs. The 1DBF with noise suppression and edge preservation preserves the P- or T-wave whereas it suppresses the QRS-interval. The 1DBF acts as a background predictor for predicting the background corresponding to the P- and T-waves and the remaining flat interval excluding the QRS-interval. The R-peak and QRS-interval are founded by the difference of the original ECG signal and the predicted background signal. Then, the Q- and S-points and the FPs related to the P- and T-wave are sequentially detected using the determined searching range and detection order based on the detected R-peak. The detection performance of the proposed method is analyzed through the MIT-BIH database (MIT-DB) and the QT database (QT-DB). Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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11 pages, 654 KiB

Open AccessArticle

Dynamical Analysis of Universal Masking on the Pandemic

by Brandon Kaiheng Tay, Carvalho Andrea Roby, Jodi Wenjiang Wu and Da Yang Tan

Int. J. Environ. Res. Public Health 2021, 18(17), 9027; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18179027 - 27 Aug 2021

Cited by 5 | Viewed by 2484

Abstract

We investigate the impact of the delay in compulsory mask wearing on the spread of COVID-19 in the community, set in the Singapore context. By using modified SEIR-based compartmental models, we focus on macroscopic population-level analysis of the relationships between the delay in compulsory mask wearing and the maximum infection, through a series of scenario-based analysis. Our analysis suggests that collective masking can meaningfully reduce the transmission of COVID-19 in the community, but only if implemented within a critical time window of approximately before 80–100 days delay after the first infection is detected, coupled with strict enforcement to ensure compliance throughout the duration. We also identify a delay threshold of about 100 days that results in masking enforcement having little significant impact on the Maximum Infected Values. The results therefore highlight the necessity for rapid implementation of compulsory mask wearing to curb the spread of the pandemic. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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19 pages, 467 KiB

Open AccessArticle

Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images

by Oliver Faust, Joel En Wei Koh, Vicnesh Jahmunah, Sukant Sabut, Edward J. Ciaccio, Arshad Majid, Ali Ali, Gregory Y. H. Lip and U. Rajendra Acharya

Int. J. Environ. Res. Public Health 2021, 18(15), 8059; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18158059 - 29 Jul 2021

Cited by 3 | Viewed by 2420

Abstract

This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, Specificity (SPE) = 95.91%, Sensitivity (SEN) = 92.44%, and Dice-score = 0.95. These results indicate that computer aided stroke severity diagnosis support is possible. Such systems might lead to progress in stroke diagnosis by enabling healthcare professionals to improve diagnosis and management of stroke patients with the same resources. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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20 pages, 4135 KiB

Open AccessArticle

Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

by Prabal Datta Barua, Nadia Fareeda Muhammad Gowdh, Kartini Rahmat, Norlisah Ramli, Wei Lin Ng, Wai Yee Chan, Mutlu Kuluozturk, Sengul Dogan, Mehmet Baygin, Orhan Yaman, Turker Tuncer, Tao Wen, Kang Hao Cheong and U. Rajendra Acharya

Int. J. Environ. Res. Public Health 2021, 18(15), 8052; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18158052 - 29 Jul 2021

Cited by 29 | Viewed by 3203

Abstract

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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12 pages, 955 KiB

Open AccessArticle

Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data

by Wen-Yu Ou Yang, Cheng-Chien Lai, Meng-Ting Tsou and Lee-Ching Hwang

Int. J. Environ. Res. Public Health 2021, 18(14), 7635; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18147635 - 18 Jul 2021

Cited by 22 | Viewed by 3803

Abstract

Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. We achieved AUROC of 0.837, 0.840, 0.843, 0.821, 0.827 in men, and 0.781, 0.807, 0.811, 0.767, 0.772 in women, for ANN, SVM, RF, KNN, and LoR models, respectively. The ANN, SVM, RF, and LoR models in men, and the ANN, SVM, and RF models in women performed significantly better than the traditional Osteoporosis Self-Assessment Tool for Asians (OSTA) model. We have demonstrated that machine learning algorithms improve the performance of screening for osteoporosis. By incorporating the models in clinical practice, patients could potentially benefit from earlier diagnosis and treatment of osteoporosis. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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11 pages, 2808 KiB

Open AccessArticle

An Automatic Approach Designed for Inference of the Underlying Cause-of-Death of Citizens

by Hui Ge, Keyan Gao, Shaoqiong Li, Wei Wang, Qiang Chen, Xialv Lin, Ziyi Huan, Xuemei Su and Xu Yang

Int. J. Environ. Res. Public Health 2021, 18(5), 2414; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18052414 - 02 Mar 2021

Cited by 5 | Viewed by 1496

Abstract

It is very important to have a comprehensive understanding of the health status of a country’s population, which helps to develop corresponding public health policies. Correct inference of the underlying cause-of-death for citizens is essential to achieve a comprehensive understanding of the health status of a country’s population. Traditionally, this relies mainly on manual methods based on medical staff’s experiences, which require a lot of resources and is not very efficient. In this work, we present our efforts to construct an automatic method to perform inferences of the underlying causes-of-death for citizens. A sink algorithm is introduced, which could perform automatic inference of the underlying cause-of-death for citizens. The results show that our sink algorithm could generate a reasonable output and outperforms other stat-of-the-art algorithms. We believe it would be very useful to greatly enhance the efficiency of correct inferences of the underlying causes-of-death for citizens. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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15 pages, 1058 KiB

Open AccessArticle

Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction

by Adrian Xi Lin, Andrew Fu Wah Ho, Kang Hao Cheong, Zengxiang Li, Wentong Cai, Marcel Lucas Chee, Yih Yng Ng, Xiaokui Xiao and Marcus Eng Hock Ong

Int. J. Environ. Res. Public Health 2020, 17(11), 4179; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17114179 - 11 Jun 2020

Cited by 31 | Viewed by 5506

Abstract

The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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Review

Jump to: Research

27 pages, 2416 KiB

Open AccessReview

Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

by Anjan Gudigar, Sneha Nayak, Jyothi Samanth, U Raghavendra, Ashwal A J, Prabal Datta Barua, Md Nazmul Hasan, Edward J. Ciaccio, Ru-San Tan and U. Rajendra Acharya

Int. J. Environ. Res. Public Health 2021, 18(19), 10003; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph181910003 - 23 Sep 2021

Cited by 17 | Viewed by 4067

Abstract

Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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20 pages, 638 KiB

Open AccessReview

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

by Joel Weijia Lai, Candice Ke En Ang, U. Rajendra Acharya and Kang Hao Cheong

Int. J. Environ. Res. Public Health 2021, 18(11), 6099; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18116099 - 05 Jun 2021

Cited by 30 | Viewed by 6811

Abstract

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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15 pages, 1049 KiB

Open AccessReview

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

by Marcel Lucas Chee, Marcus Eng Hock Ong, Fahad Javaid Siddiqui, Zhongheng Zhang, Shir Lynn Lim, Andrew Fu Wah Ho and Nan Liu

Int. J. Environ. Res. Public Health 2021, 18(9), 4749; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18094749 - 29 Apr 2021

Cited by 16 | Viewed by 4357

Abstract

Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic. Full article

(This article belongs to the Special Issue Simulations and Machine Learning via Big Data for Prediction, Detection, Treatment or Rehabilitation in the Healthcare Process)

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