scholarly journals Identifying Functional Profiles of Challenging Behaviors in Autism Spectrum Disorder with Unsupervised Machine Learning

2021 ◽  
Author(s):  
◽  
Emily Daskas
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Challenging Behaviors ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Unsupervised Machine Learning ◽  
Functional Profiles

scholarly journals Identifying Challenging Behavior Profiles and Exploring their Impact on Treatment Efficacy in Autism Spectrum Disorder using Unsupervised Machine Learning (Preprint)

2021 ◽  
Author(s):  
Julie Gardner-Hoag ◽  
Marlena Novack ◽  
Chelsea Parlett-Pelleriti ◽  
Elizabeth Stevens ◽  
Dennis Dixon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Treatment Response ◽  
Challenging Behavior ◽  
Challenging Behaviors ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Unsupervised Machine Learning ◽  
Skill Mastery ◽  
The Impact

BACKGROUND Challenging behaviors are prevalent among individuals with autism spectrum disorder (ASD); however, research exploring the impact of challenging behaviors on treatment response is lacking. OBJECTIVE The purpose of the current study was to identify subtypes of ASD based on engagement in different challenging behaviors and evaluate differences in treatment response between subgroups. METHODS Retrospective data on challenging behaviors and treatment progress for 854 children with ASD were analyzed. First, participants were clustered based on eight observed challenging behaviors using k-means. Next, a multiple linear regression analysis was performed to find significant interactions between skill mastery and treatment hours, cluster assignment, and gender. RESULTS Seven diverse clusters were identified, which demonstrated a single dominant challenging behavior. For some clusters, significant differences in treatment response were found. Specifically, a cluster characterized by stereotypy was found to have significantly higher levels of skill mastery than clusters characterized by self-injurious behavior and aggression. CONCLUSIONS These findings have implications on the treatment of individuals with ASD. First, self-injurious behavior and aggression were prevalent among participants with the poorest treatment response, thus interventions targeting these challenging behaviors may be worth prioritizing. Furthermore, the use of unsupervised machine learning models to identify subtypes of ASD shows promise.

scholarly journals Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning

2019 ◽  
Vol 129 ◽  
pp. 29-36 ◽  
Author(s):  
Elizabeth Stevens ◽  
Dennis R. Dixon ◽  
Marlena N. Novack ◽  
Doreen Granpeesheh ◽  
Tristram Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Unsupervised Machine Learning ◽  
Behavioral Phenotypes

scholarly journals Supporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review

2021 ◽  
Author(s):  
Rita Francese ◽  
Xiaomin Yang
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Challenging Behaviors ◽  
Research Work ◽  
Economic Loss ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Noninvasive Sensors

AbstractThe number of autism spectrum disorder individuals is dramatically increasing. For them, it is difficult to get an early diagnosis or to intervene for preventing challenging behaviors, which may be the cause of social isolation and economic loss for all their family. This SLR aims at understanding and summarizing the current research work on this topic and analyze the limitations and open challenges to address future work. We consider papers published between 2015 and the beginning of 2021. The initial selection included about 2140 papers. 11 of them respected our selection criteria. The papers have been analyzed by mainly considering: (1) the kind of action taken on the autistic individual, (2) the considered wearables, (3) the machine learning approaches, and (4) the evaluation strategies. Results revealed that the topic is very relevant, but there are many limitations in the considered studies, such as reduced number of participants, absence of datasets and experimentation in real contexts, need for considering privacy issues, and the adoption of appropriate validation approaches. The issues highlighted in this analysis may be useful for improving machine learning techniques and highlighting areas of interest in which experimenting with the use of different noninvasive sensors.

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

Oppositional Defiant Disorder and Related Disruptive Behaviors in Autism Spectrum Disorder

2020 ◽  
pp. 136-158
Author(s):  
Connor M. Kerns ◽  
Chandler Puhy ◽  
Chelsea M. Day ◽  
Steven J. Berkowitz
Keyword(s):  
Autism Spectrum Disorder ◽  
Oppositional Defiant Disorder ◽  
Challenging Behaviors ◽  
Disruptive Behaviors ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Future Research ◽  
Oppositional Defiant ◽  
Assessment And Treatment ◽  
Defiant Behavior

The Diagnostic and Statistical Manual of Mental Disorders, fifth edition characterizes oppositional defiant disorder (ODD) as reflecting pervasive patterns of irritable mood, defiant behavior, and/or vindictiveness. Youth with autism spectrum disorder (ASD) exhibit high rates of disruptive behaviors commonly associated with ODD, such as noncompliance, irritability, temper tantrums, and mood dysregulation. This chapter reviews the presentation of ODD in individuals with ASD, including current prevalence estimates, proposed etiology, validated assessment methods, and emerging best practices designed to treat challenging behaviors. Although there is a robust literature describing assessment and treatment procedures for disruptive behaviors in individuals with ASD, conceptualizing these hallmark behaviors within the framework of ODD is relatively novel and not without controversy. Discussion thus includes challenges around the applicability of the diagnostic criteria in this population and future research directions that may provide clarity on this issue.

Teaching Social Scripts to Improve Social Communication for Students With Autism Spectrum Disorder

2021 ◽  
pp. 004005992110220
Author(s):  
Gretchen Scheibel ◽  
Zijie Ma ◽  
Jason C. Travers
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Challenging Behaviors ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Evidence Based ◽  
Academic Difficulties ◽  
Evidence Based Practices ◽  
Social Scripts ◽  
Students With Autism

Students with Autism Spectrum Disorder are likely to demonstrate social impairments that contribute to challenging behaviors and academic difficulties. As a result, the task of improving social communication skills is a critical component to any educational program for this population. Scripting provides an evidence-based and versatile option for improving social communication, yielding valuable results while requiring limited time and resource preparation from educators. In this article, we present step by step guidance to support practitioners in using scripting interventions. Considerations are discussed for adapting this intervention to meet the needs of students across the autism spectrum and links to resources for strengthening implementation and including other evidence-based practices.

scholarly journals Lagging skills contribute to challenging behaviors in children with autism spectrum disorder without intellectual disability

Autism ◽  
2017 ◽  
Vol 22 (8) ◽  
pp. 898-906 ◽  
Author(s):  
Brenna B Maddox ◽  
Patrick Cleary ◽  
Emily S Kuschner ◽  
Judith S Miller ◽  
Anna Chelsea Armour ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Emotion Regulation ◽  
Executive Function ◽  
Intellectual Disability ◽  
Social Skills ◽  
Challenging Behaviors ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Environmental Demands

Many children with autism spectrum disorder display challenging behaviors. These behaviors are not limited to those with cognitive and/or language impairments. The Collaborative and Proactive Solutions framework proposes that challenging behaviors result from an incompatibility between environmental demands and a child’s “lagging skills.” The primary Collaborative and Proactive Solutions lagging skills—executive function, emotion regulation, language, and social skills—are often areas of weakness for individuals with autism spectrum disorder. The purpose of this study was to evaluate whether these lagging skills are associated with challenging behaviors in youth with autism spectrum disorder without intellectual disability. Parents of 182 youth with autism spectrum disorder (6–15 years) completed measures of their children’s challenging behaviors, executive function, language, emotion regulation, and social skills. We tested whether the Collaborative and Proactive Solutions lagging skills predicted challenging behaviors using multiple linear regression. The Collaborative and Proactive Solutions lagging skills explained significant variance in participants’ challenging behaviors. The Depression (emotion regulation), Inhibit (executive function), and Sameness (executive function) scales emerged as significant predictors. Impairments in emotion regulation and executive function may contribute substantially to aggressive and oppositional behaviors in school-age youth with autism spectrum disorder without intellectual disability. Treatment for challenging behaviors in this group may consider targeting the incompatibility between environmental demands and a child’s lagging skills.

scholarly journals Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

2020 ◽  
Author(s):  
Haishuai Wang ◽  
Paul Avillach
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Screening Tools ◽  
Common Variants ◽  
Autism Screening

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

scholarly journals Vocal markers of Autism Spectrum Disorder: assessing the generalizability of machine learning models

2021 ◽  
Author(s):  
Astrid Rybner ◽  
Emil Trenckner Jessen ◽  
Marie Damsgaard Mortensen ◽  
Stine Nyhus Larsen ◽  
Ruth Grossman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Model Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Future Studies ◽  
Out Of Sample ◽  
Biobehavioral Markers ◽  
Similar Task ◽  
Machine Learning Models

Background: Machine learning (ML) approaches show increasing promise to identify vocal markers of Autism Spectrum Disorder (ASD). Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected in diverse settings such as using a different speech task or a different language. Aim: In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. Methods: We re-train a promising published ML model of vocal markers of ASD on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on i) different participants from the same study, performing the same task; ii) the same participants, performing a different (but similar) task; iii) a different study with participants speaking a different language, performing the same type of task. Results: While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to new similar tasks and not at all to new languages. The ML pipeline is openly shared. Conclusion: Generalizability of ML models of vocal markers - and more generally biobehavioral markers - of ASD is an issue. We outline three recommendations researchers could take in order to be more explicit about generalizability and improve it in future studies.

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