Global trends and hotspots in the digital therapeutics of autism spectrum disorders: a bibliometric analysis from 2002 to 2022

Frontiers in Psychiatry, Journal Year: 2023, Volume and Issue: 14

Published: May 15, 2023

Introduction Autism spectrum disorder (ASD) is a severe neurodevelopmental that has become major cause of disability in children. Digital therapeutics (DTx) delivers evidence-based therapeutic interventions to patients are driven by software prevent, manage, or treat medical disease. This study objectively analyzed the current research status global DTx ASD from 2002 2022, aiming explore and trends field. Methods The Web Science database was searched for articles about January October 2022. CiteSpace used analyze co-occurrence keywords literature, partnerships between authors, institutions, countries, sudden occurrence keywords, clustering over time, analysis references, cited journals. Results A total 509 were included. most productive country institution United States Vanderbilt University. largest contributing authors Warren, Zachary, Sarkar, Nilanjan. most-cited journal Journal Developmental Disorders . co-cited Brian Scarselati (Robots Use Research, 2012) Ralph Adolphs (Abnormal processing social information faces autism, 2001). “Artificial Intelligence,” “machine learning,” “Virtual Reality,” “eye tracking” common new cutting-edge on ASD. Discussion use developing rapidly gaining attention researchers worldwide. publications this field have increased year year, mainly concentrated developed especially States. Both University Yale very important institutions researcher University, Warren his dynamics achievements also more worth our attention. application technologies such as virtual reality, machine learning, eye-tracking development currently popular topic. More cross-regional cross-disciplinary collaborations recommended advance availability DTx.

Language: Английский

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Towards Trustworthy Myopia Detection: Integration Methodology of Deep Learning Approach, XAI Visualization, and User Interface System

Applied Data Science and Analysis, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 15

Published: Feb. 23, 2023

Myopia, a prevalent vision disorder with potential complications if untreated, requires early and accurate detection for effective treatment. However, traditional diagnostic methods often lack trustworthiness explainability, leading to biases mistrust. This study presents four-phase methodology develop robust myopia system. In the initial phase, dataset containing training testing images is located, preprocessed, balanced. Subsequently, two models are deployed: pre-trained VGG16 model renowned image classification tasks, sequential CNN convolution layers. Performance evaluation metrics such as accuracy, recall, F1-Score, sensitivity, logloss utilized assess models' effectiveness. The third phase integrates trustworthiness, transparency through application of Explainable Artificial Intelligence (XAI) techniques. Specifically, Local Interpretable Model-Agnostic Explanations (LIME) employed provide insights into decision-making process deep learning model, offering explanations myopic or normal. final user interface implemented XAI bringing together aforementioned phases. outcomes this contribute advancement objective explainable in field detection. Notably, achieves an impressive accuracy 96%, highlighting its efficacy diagnosing myopia. LIME results valuable interpretations cases. proposed enhances transparency, interpretability, trust process.

Language: Английский

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Explainable Artificial Intelligence Multimodal of Autism Triage Levels Using Fuzzy Approach-Based Multi-criteria Decision-Making and LIME

International Journal of Fuzzy Systems, Journal Year: 2023, Volume and Issue: 26(1), P. 274 - 303

Published: Nov. 17, 2023

Language: Английский

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Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications

International Journal of Computational Intelligence Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: June 17, 2024

Abstract In the context of autism spectrum disorder (ASD) triage, robustness machine learning (ML) models is a paramount concern. Ensuring ML faces issues such as model selection, criterion importance, trade-offs, and conflicts in evaluation benchmarking models. Furthermore, development must contend with two real-time scenarios: normal tests adversarial attack cases. This study addresses this challenge by integrating three key phases that bridge domains fuzzy multicriteria decision-making (MCDM). First, utilized dataset comprises authentic information, encompassing 19 medical sociodemographic features from 1296 autistic patients who received diagnoses via intelligent triage method. These were categorized into one labels: urgent, moderate, or minor. We employ principal component analysis (PCA) algorithms to fuse large number features. Second, fused forms basis for rigorously testing eight models, considering scenarios, evaluating classifier performance using nine metrics. The third phase developed robust framework encompasses creation decision matrix (DM) 2-tuple linguistic Fermatean opinion score method (2TLFFDOSM) multiple-ML perspectives, accomplished through individual external group aggregation ranks. Our findings highlight effectiveness PCA algorithms, yielding 12 components acceptable variance. ranking, logistic regression (LR) emerged top-performing terms 2TLFFDOSM (1.3370). A comparative five benchmark studies demonstrated superior our across all six checklist comparison points.

Language: Английский

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Fuzzy Decision‐Making Framework for Evaluating Hybrid Detection Models of Trauma Patients

Expert Systems, Journal Year: 2025, Volume and Issue: 42(3)

Published: Feb. 13, 2025

ABSTRACT This study introduces a new multi‐criteria decision‐making (MCDM) framework to evaluate trauma injury detection models in intensive care units (ICUs). research addresses the challenges associated with diverse machine learning (ML) models, inconsistencies, conflicting priorities, and importance of metrics. The developed methodology consists three phases: dataset identification pre‐processing, hybrid model development, an evaluation/benchmarking framework. Through meticulous is tailored focus on adult patients. Forty were by combining eight ML algorithms four filter‐based feature‐selection methods principal component analysis (PCA) as dimensionality reduction method, these evaluated using seven weight coefficients for metrics are determined 2‐tuple Linguistic Fermatean Fuzzy‐Weighted Zero‐Inconsistency (2TLF‐FWZIC) method. Vlsekriterijumska Optimizcija I Kompromisno Resenje (VIKOR) approach applied rank models. According 2TLF‐FWZIC, classification accuracy (CA) precision obtained highest weights 0.2439 0.1805, respectively, while F1, training time, test time lowest 0.1055, 0.0886, 0.1111, respectively. benchmarking results revealed following top‐performing models: Gini index logistic regression (GI‐LR), decision tree (GI_DT), information gain (IG_DT), VIKOR Q score values 0.016435, 0.023804, 0.042077, proposed MCDM assessed examined systematic ranking, sensitivity analysis, validation best‐selected two unseen datasets, mode explainability SHapley Additive exPlanations (SHAP) We benchmarked against other benchmark studies achieved 100% across six key areas. provides several insights into empirical synthesis this study. It contributes advancing medical informatics enhancing understanding selection ICUs.

Language: Английский

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Innovative Deep Learning Approaches in Autism Research

Published: Jan. 1, 2025

Language: Английский

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Deep Learning Insights into ASD: Classifying and Unveiling Behavioural Patterns through RoBERTa and Topic Modeling on QCHAT Data.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: March 15, 2024

Abstract This study leverages advanced Natural Language Processing (NLP) models, including Bidirectional Encoder Representations from Transformers (BERT), A Robustly Optimized BERT Pretraining Approach (RoBERTa), and Topic Modeling, to analyze behavioral patterns in Autism Spectrum Disorder (ASD). Using the Quantitative Checklist for Toddlers (QCHAT) dataset enhanced with ASD-related terms, we demonstrate potential of these models improve ASD vs. Typically Developing (TD) classification accuracy uncover key themes indicative ASD. Our findings highlight value enriching clinical datasets domain-specific knowledge showcase power adapting deep learning techniques research. work contributes developing more accurate informative diagnostic tools.

Language: Английский

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Evaluation and benchmarking of hybrid machine learning models for autism spectrum disorder diagnosis using a 2-tuple linguistic neutrosophic fuzzy sets-based decision-making model

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(29), P. 18161 - 18200

Published: July 20, 2024

Language: Английский

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Unlocking the Potential of Autism Detection: Integrating Traditional Feature Selection and Machine Learning Techniques

Applied Data Science and Analysis, Journal Year: 2023, Volume and Issue: unknown, P. 42 - 58

Published: May 1, 2023

The diagnostic process for Autism Spectrum Disorder (ASD) typically involves time-consuming assessments conducted by specialized physicians. To improve the efficiency of ASD screening, intelligent solutions based on machine learning have been proposed in literature. However, many existing ML models lack incorporation medical tests and demographic features, which could potentially enhance their detection capabilities considering affected features through traditional feature selection approaches. This study aims to address aforementioned limitation utilizing a real dataset containing 45 983 patients. achieve this goal, two-phase methodology is employed. first phase data preparation, including handling missing model-based imputation, normalizing using Min-Max method, selecting relevant approaches features. In second phase, seven classification techniques recommended literature, Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, Gradient Boosting (GB), Neural Network (NN), are utilized develop models. These then trained tested prepared evaluate performance detecting ASD. assessed various metrics, such as Accuracy, Recall, Precision, F1-score, AUC, Train time, Test time. metrics provide insights into models' overall accuracy, sensitivity, specificity, trade-off between true positive false rates. results highlight effectiveness Specifically, GB model outperforms other with an accuracy 87%, Recall Precision 86%, F1-score AUC 95%, time 21.890, 0.173. Additionally, benchmarking analysis against five studies reveals that achieves perfect score across three key areas. By approaches, developed demonstrate improved potential screening diagnosis processes.

Language: Английский

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Developing an Artificial Intelligence Based Model for Autism Spectrum Disorder Detection in Children

Journal of Advanced Research in Applied Sciences and Engineering Technology, Journal Year: 2023, Volume and Issue: 32(1), P. 57 - 72

Published: Aug. 19, 2023

Sensory difficulties, such as an over or under responsiveness to noises, smells, touch, are frequently present in individuals with Autism Spectrum Disease (ASD), a neurodevelopmental disorder. The condition's primary cause is hereditary, however early diagnosis and therapy can assist. Traditional clinical procedures may be expensive time consuming, but current history, deep learning based sophisticated has emerged supplement them. goal of this study streamline the diagnostic procedure by identifying most important characteristics automating them using existing classification methods. We have looked at datasets including toddlers, kids, teens, adults autism spectrum To find highest performing feature set for these four ASD datasets, we compared state-of-the-art categorization selection Across adults, our experiments reveal that multilayer perceptron (MLP) classifier achieves 100% accuracy fewest possible features. also determine proposed approach ranks across all datasets.

Language: Английский

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