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The exact nature, harmful effects and aetiology of autism spectrum disorder (ASD) have caused widespread confusion. Artificial intelligence (AI) science helps solve challenging diagnostic problems in the medical field through extensive experiments. Disease severity is closely related to triage decisions and prioritisation contexts in medicine because both have been widely used to diagnose various diseases via AI, machine learning and automated decision-making techniques. Recently, taking advantage of high-performance AI algorithms has achieved accessible success in diagnosing and predicting risks from clinical and biological data. In contrast, less progress has been made with ASD because of obscure reasons. According to academic literature, ASD diagnosis works from a specific perspective, and much of the confusion arises from the fact that how AI techniques are currently integrated with the diagnosis of ASD concerning the triage and priority strategies and gene contributions. To this end, this study sought to describe a systematic review of the literature to assess the respective AI methods using the available datasets, highlight the tools and strategies used for diagnosing ASD and investigate how AI trends contribute in distinguishing triage and priority for ASD and gene contributions. Accordingly, this study checked the Science Direct, IEEE Xplore Digital Library, Web of Science (WoS), PubMed, and Scopus databases. A set of 363 articles from 2017 to 2022 is collected to reveal a clear picture and a better understanding of all the academic literature through a final set of 18 articles. The retrieved articles were filtered according to the defined inclusion and exclusion criteria and classified into three categories. The first category includes ‘Triage patients based on diagnosis methods’ which accounts for 16.66% (n = 3/18). The second category includes ‘Prioritisation for Risky Genes’ which accounts for 66.6% (n = 12/18) and is classified into two subcategories: ‘Mutations observation based’, ‘Biomarkers and toxic chemical observations’. The third category includes ‘E-triage using telehealth’ which accounts for 16.66% (n = 3/18). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations and challenges of ASD research that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and discusses the open issues that help perform and improve the recommended solution of ASD research direction. In addition, this study critically reviews the literature and attempts to address the current research gaps in knowledge and highlights weaknesses that require further research. Finally, a new developed methodology has been suggested as future work for triaging and prioritising ASD patients according to their severity levels by using decision-making techniques. •Construction of new taxonomy for the triage and prioritisation of ASD.•Highlights motivations, challenges and recommendations to reduce complexity of health autism diagnosis used AI technologies.•States five complex and important open issues associated with the triage and prioritisation of ASD.•Offering conceptual proposal for triage and prioritisation for autism patients based on MCDM methods.

During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely utilised because of its usability and safety in providing healthcare services during the COVID-19 pandemic. However, a systematic literature review which provides extensive evidence on the impact of COVID-19 through telehealth and which covers multiple directions in a large-scale research remains lacking. This study aims to review telehealth literature comprehensively since the pandemic started. It also aims to map the research landscape into a coherent taxonomy and characterise this emerging field in terms of motivations, open challenges and recommendations. Articles related to telehealth during the COVID-19 pandemic were systematically searched in the WOS, IEEE, Science Direct, Springer and Scopus databases. The final set included (n = 86) articles discussing telehealth applications with respect to (i) control (n = 25), (ii) technology (n = 14) and (iii) medical procedure (n = 47). Since the beginning of the pandemic, telehealth has been presented in diverse cases. However, it still warrants further attention. Regardless of category, the articles focused on the challenges which hinder the maximisation of telehealth in such times and how to address them. With the rapid increase in the utilization of telehealth in different specialised hospitals and clinics, a potential framework which reflects the authors’ implications of the future application and opportunities of telehealth has been established. This article improves our understanding and reveals the full potential of telehealth during these difficult times and beyond. •State-of-the-art Literature Categorization for Telehealth utilization during COVID-19.•Challenges, motivations and recommended solutions are identified for Telehealth during COVID-19.•Different Applications of Telehealth during the COVID-19 pandemic.

Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” (n=39). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” (n=1). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.

This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values \"C12 = Laughing for no reason\" and \"C16 = Notice the sound of the bell\" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

The artificial intelligence (AI) trend to embrace Autism Spectrum Disorder (ASD) has dramatically transformed the landscape of medical diagnosis. People often exhibit fear and apprehension towards conditions they lack understanding of, and ASD being a complex affliction, poses challenges in comprehending its intricacies. Researchers have harnessed AI applications to improve the precision of disease diagnosis by utilizing Magnetic Resonance Imaging (MRI), Electroencephalography (EEG), genetic, sociodemographic, and medical data. However, the development of AI systems for early diagnosis and triage in healthcare is still in its nascent stages. In particular, studies have revealed a global increase in the prevalence of ASD, with an estimated 1 in 59 children being diagnosed. However, there is a lack of up-to-date information regarding the current status of ASD. This study aims to provide a systematic review of AI applications in early diagnosis and triage for ASD, supplementing the findings of previous studies and offering a comprehensive overview of the evidence. To achieve this, a rigorous literature search method and selection criteria were employed, resulting in the identification of 46 recent contributions on the applications of AI in ASD from various databases, including ScienceDirect (SD), IEEE Xplore digital library (IEEE), Web of Science (WOS), PubMed, and Scopus. The selected papers were categorized into three main categories: ASD triage levels, clinical diagnosis for ASD, and diagnosis based on telemedicine, with further subcategories under the clinical diagnosis category. Theoretical and practical aspects of AI methods used for ASD diagnosis, as well as the presentation utilizing data analytics, were presented. The paper presents a systematic and comprehensive analysis of previous studies, examining the challenges, motivations, and recommendations, thereby paving the way for potential future research. Additionally, the work provides decisive evidence for the use of AI in ASD healthcare diagnosis and triage, offering nine critical analyses of the current state-of-the-art and addressing relevant research gaps. To the best of our knowledge, this study is innovative in exploring the feasibility of using AI in ASD medical diagnosis and triage. It highlights essential pieces of information, including Explainable AI (XAI), Auto machine learning (AutoML), Internet of Things (IoT)-based AI, robot-assisted therapy-based AI, telemedicine, data fusion techniques, and available ASD datasets with different aspects. The analysis of the revised contributions reveals crucial implications for academics and practitioners. The paper also proposes potential methodological aspects to enhance the triage and prioritization of autistic patients using AI applications in the medical sector, as well as addressing theoretical and practical application aspects and five methodology phases using fuzzy Multi-Criteria Decision Making (MCDM) methods in ASD triage and prioritization.

Hybridised classification and prioritisation of patients with chronic heart diseases (CHDs) can save lives by categorising them on the basis of disease severity and determining priority patients. Such hybridisation is challenging and thus has not been reported in the literature on telemedicine. This paper presents an intelligent classification and prioritisation framework for patients with CHDs who engage in telemedicine. The emergency status of 500 patients with CHDs was evaluated on the basis of multiple heterogeneous clinical parameters, such as electrocardiogram, oxygen saturation, blood pressure and non-sensory measurements (i.e. text frame), by using wearable sensors. In the first stage, the patients were classified according to Dempster–Shafer theory and separated into five categories, namely, at high risk, requires urgent care, sick, in a cold state and normal. In the second stage, hybridised multi-criteria decision-making models, namely, multi-layer analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS), were used to prioritise patients according to their emergency status. Then, the priority patients were queued in each emergency category according to the results of the first stage. Results demonstrated that Dempster–Shafer theory and the hybridised MLAHP and TOPSIS model are suitable for classifying and prioritising patients with CHDs. Moreover, the groups’ scores in each category showed remarkable differences, indicating that the framework results were identical. The proposed framework has an advantage over other benchmark classification frameworks by 33.33% and 50%, and an advantage over earlier benchmark prioritisation by 50%. This framework should be considered in future studies on telemedicine architecture to improve healthcare management.

Autism spectrum disorder (ASD) presents challenges for accurate diagnosis, prompting researchers to search for an optimal diagnostic process. Feature selection (FS) approaches and classification methods considering medical tests and socio-demographic characteristics are crucial for diagnosing autism. However, evaluating and benchmarking hybrid diagnosis machine learning (ML) models in the presence of multiple evaluation performance metrics, criteria trade-offs, and varying criteria importance present complex multi-criteria decision-making (MCDM) problems. This study proposes a three-phase methodology integrating FS, ML, and fuzzy MCDM to develop and evaluate diagnosis models. Firstly, an ASD dataset combining medical tests and socio-demographic characteristics is identified and preprocessed. Secondly, 72 hybrid diagnosis models are developed by combining eight FS techniques and nine ML algorithms using an intersection process. Thirdly, the following steps are performed: (i) A decision matrix is formulated based on nine evaluation metrics, including classification accuracy (CA), specificity, precision, F1 score, recall, test time, train time, log loss, and area under the curve (AUC); (ii) a new extension of fuzzy-weighted zero inconsistency is developed using 2-tuple linguistic neutrosophic fuzzy sets (2TLNFSs) to assign weights to the evaluation metrics criteria and address related issues; (iii) a new extension of the fuzzy decision-by-opinion score method is developed using 2TLNFSs as well to benchmark the 72 models. Results indicate that the selected FS techniques vary in the number of features chosen, with the sets ranging from 19 to 46 out of the 48 available features. Socio-demographic features were predominantly selected over medical tests. Regarding the evaluation and benchmarking results, the weights constructed by three experts suggest that CA holds high importance, precision and recall are assigned equal weights, and AUC and test time carry moderate weights. At the same time, F1 and log loss are considered less crucial in the decision-making process. Specificity and train time are assigned relatively lower weights, indicating their lower importance. The best-performing hybrid model identified was sequential feature selection/logistic regression (SFS/LR)-decision tree, with a score value of 4.3964. Decision trees and gradient boosting consistently achieved high rankings, demonstrating their effectiveness in diagnosing ASD, while SVM, random forest, and logistic regression showed mixed results across different hybrid models. The sensitivity analysis assessments were conducted to verify the efficiency of the proposed evaluation and benchmarking methodology. We benchmarked the proposed framework against three other benchmark studies and achieved a score of 100% across five key areas. The developed methodology can potentially advance and accelerate the selection of diagnostic tools for ASD therapy, benefiting individuals with ASD.

Children with autism spectrum disorders (ASDs) tremendously impact people’s lives, and the incidence and prevalence of ASDs are increasing globally. Global health organisations and other autism-treatment centres specialising in autism diagnosis and detection are encountering challenges on how to provide an appropriate ASD diagnosis system that enables accurate analyses and early detection of autism. Information about ASD detection is affected by unknown aetiology of the disease, and an urgent solution is required to investigate its aetiological factors. Accordingly, increasing the opportunities to provide evidence of the ‘sociodemographic and family characteristics’ risk factors in predicting ASD is a scientific complex problem that needs to be solved. This study developed an early prediction model for diagnosing and detecting children with ASD based on effective sociodemographic and family characteristic features related to ASD using the machine learning (ML) model. The proposed methodology involves three phases. The identification phase is first accomplished by identifying a large-scale ASD dataset and preprocessing stages: 1-NN model for imputing missing data, feature-selection methods using Chi2 and Relief, and adaptive balancing data approach using Synthetic Minority Oversampling Technique. Chi2 and Relief are applied to determine the most effective sociodemographic and family characteristic features and produce a new balanced ASD dataset. The second development phase trains and tests the newly prepared ASD dataset through eight ML methods: decision tree, random forest, Naive Bayes, kNN, SVM, logistic regression, AdaBoost, and neural network multilayer perceptron (MLP). The developed model is evaluated in the third phase using five metrics: accuracy, precision, recall, F1 and AUROC, and test time in seconds. Results indicated the following: (1) Out of 10 highly effective sociodemographic and family characteristic features, seven related to autism cases are extracted. (2) Correlation sensitivity analysis reveals that the ‘ Mom_age_at_child_birth ’ has the highest positive correlation with ‘ Father_age_at_child_birth ,’ with an r -value of 0.751. Moreover, ‘child_birth_month’ and ‘ Birth_number ’ have the highest negative correlation with ‘ Ses_points_1_10 ’, with an r -value of (− 0.07). (3) AdaBoost, neural network, K-nearest neighbour, and decision tree methods show higher accuracy results (0.9995, 0.9925, 0.9834, and 0.9786, respectively), whereas random forest, logistic regression, and Naive Bayes methods show relatively lower accuracy (0.8297, 0.8199 and 0.8002, respectively). However, the support vector machine method shows the lowest accuracy (0.7105). AdaBoost obtained the highest accuracy on the basis of four other evaluation metrics (AUC = 0.9999, F 1 = 0.9995, precision = 0.9995 and recall = 0.9995). Accordingly, the new preprocessed and balanced ASD dataset can be utilised as a data source for autism research. The preprocessing stages can be considered correct and successfully perform better results than the original ASD dataset. Similar results from Chi2 and Relief in the feature-selection approaches substantially improved the classification accuracy. The study confirms the efficacy of the proposed prediction model compared with previous models in different comparative points. Early prediction of autism is possible through this proposed model.

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, includes intelligent decision-making tools for evaluation and benchmarking. Rather than just finding a comparable or similar analogy, this framework is intended to help guide future research and pragmatic applications, and to determine which SSVEP-based BCI applications have succeeded at responsibility for what they set out with.