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

Machine learning and deep learning methods are becoming increasingly used in the understanding, identification, and improvement of the diagnosis and treatment of Autism Spectrum Disorder. People with ASD often exemplify challenging behaviors that can put their safety, education, and general quality of life at risk. Challenging behaviors are driven by one of four functions. The combination of common occurrences of challenging behaviors and their respective behavioral functions are unique to the individual and circumstance, and the most successful therapies account for both challenging behaviors and their respective functions. Therefore, it is important that research is done on these concepts to lead to improvements in therapy and outcomes. In this thesis, we apply a cluster analysis to a sample of 1,416 individuals with Autism Spectrum Disorder. The aim is to find groupings of patients based on the relative frequency of each unique challenging behavior and function pair. As the first machine learning study to focus on combining the behavioral functions and challenging behaviors of ASD, we find that there are some patterns to be found based on eight identified clusters. The results of the study could impact the way that treatment and therapy plans are paved for children with Autism Spectrum Disorder

A Cluster Analysis of Challenging Behaviors in Autism Spectrum Disorder

We apply cluster analysis to a sample of 2,116 children with Autism Spectrum Disorder in order to identify patterns of challenging behaviors observed in home and centerbased clinical settings. The largest study of this type to date, and the first to employ machine learning, our results indicate that while the presence of multiple challenging behaviors is common, in most cases a dominant behavior emerges. Furthermore, the trend is also observed when we train our cluster models on the male and female samples separately. This work provides a basis for future studies to understand the relationship of challenging behavior profiles to learning outcomes, with the ultimate goal of providing personalized therapeutic interventions with maximum efficacy and minimum time and cost

Classifying Challenging Behaviors in Autism Spectrum Disorder with Neural Document Embeddings

The understanding and treatment of challenging behaviors in individuals with Autism Spectrum Disorder is paramount to enabling the success of behavioral therapy; an essential step in this process being the labeling of challenging behaviors demonstrated in therapy sessions. These manifestations differ across individuals and within individuals over time and thus, the appropriate classification of a challenging behavior when considering purely qualitative factors can be unclear. In this thesis we seek to add quantitative depth to this otherwise qualitative task of challenging behavior classification. We do so through the application of natural language processing techniques to behavioral descriptions extracted from the CARD Skills dataset. Specifically, we construct 3 sets of 50-dimensional document embeddings to represent the 1,917 recorded instances of challenging behaviors demonstrated in Applied Behavior Analysis therapy. These embeddings are learned through three processes: a TF-IDF weighted sum of Word2Vec embeddings, Doc2Vec embeddings which use hierarchical softmax as an output layer, and Doc2Vec which optimizes the original Doc2Vec architecture through Negative Sampling. Once created, these embeddings are initially used as input to a Support Vector Machine classifier to demonstrate the success of binary classification within this problem set. This preliminary exploration achieves promising classification accuracies ranging from 78.2-100% and establishes the separability of challenging behaviors given their neural embeddings. We next construct a multi-class classification model via a Gaussian Process Classifier fitted with Laplace approximation. This classification model, trained on an 80/20 stratified split of the seven most frequently occurring behaviors in the dataset, produces an accuracy of 82.7%. Through this exploration we demonstrate that the semantic queues derived from the language of challenging behavior descriptions, modeled using natural language processing techniques, can be successfully leveraged in classification architectures. This study represents the first of its kind, providing a proof of concept for the application of machine learning to the observations of challenging behaviors demonstrated in ASD with the ultimate goal of improving the efficacy of the behavioral treatments which intrinsically rely on the accurate identification of these behaviors

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review

Autism spectrum disorder (ASD) research has yet to leverage big data on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data

Tutorial : applying machine learning in behavioral research

Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets

Gaining Computational Insight into Psychological Data: Applications of Machine Learning with Eating Disorders and Autism Spectrum Disorder

Over the past 100 years, assessment tools have been developed that allow us to explore mental and behavioral processes that could not be measured before. However, conventional statistical models used for psychological data are lacking in thoroughness and predictability. This provides a perfect opportunity to use machine learning to study the data in a novel way. In this paper, we present examples of using machine learning techniques with data in three areas: eating disorders, body satisfaction, and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR). Our first study employs the k-means clustering algorithm to explore eating disorder behaviors. Our results show that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) are good predictors of eating disorder behavior. Our second study uses a hierarchical clustering algorithm to find patterns in the dataset that were previously not considered. We found four clusters that may highlight the unique differences between participants who had positive body image versus participants who had negative body image. The final chapter presents a case study with a specific VR tool, Bob’s Fish Shop, and users with ASD and Attention Deficit Hyperactivity Disorder (ADHD). We hypothesize that, through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding. Through the implementation of various machine learning algorithms and programs, we can study the human experience in a way that has never been done. We can account for neurodiverse populations and assist them in ways that are not only helpful but also educational

The effects of auditory-motor mapping training on speech output of nonverbal elementary age students with autism spectrum disorder

The purpose of this study was to investigate the effect of auditory-motor mapping training (AMMT) on the speech output of nonverbal elementary age students with autism spectrum disorder (ASD). Auditory-motor mapping training facilitates the development of association between sounds and articulatory actions using intonation and bimanual drumming activities. This intervention purportedly stimulates neural networks that may be dysfunctional in persons with ASD. Seven nonverbal children with a primary diagnosis of ASD participated in twelve 20-minute weekly sessions consisting of engagement with 15 predetermined target words through imitation, singing, and motor activity (all components of AMMT). Assessments were made at baseline, mid-point, and post AMMT intervention sessions. These probes were used to determine the effects of AMMT on expressive language abilities of speech output. A null hypothesis was tested to determine the significance of the independent variables of singing, showing visual cues, and drumming on the speech output of nonverbal children with ASD, age five through eight years (p = .05). Additionally, effects of AMMT on children's development of social communication skills also were examined at the end of each intervention session. Results of the study revealed no significant effect of the AMMT intervention on the speech output of elementary age children with ASD from the best baseline to probe one and probe two (p = .424), therefore the null hypothesis that there was no significant effect of auditory-motor mapping training (AMMT) on speech output of nonverbal elementary children with ASD was retained. Additionally, a comparison of the growth of the independent ‘High Five’ gesture from session one to session twelve yielded no statistical significant results (p > .05). The McNemar chi-square was used to compare this secondary AMMT effect from sessions two to eleven, and revealed a positive growth trend that approached a significant outcome associated with the children's social communication responses (p =.063). Although significant changes in the nonverbal children's speech output were not substantiated in this study, there were areas of growth for all children in this study that were highlighted through qualitative analysis and descriptive narratives. Confounding variables that possibly affected children's speech output and social communication development were addressed. Additionally, recommendations were made for future research involving music as a vehicle for speech development for nonverbal elementary age children with ASD

Computational modelling of interventions for developmental disorders

We evaluate the potential of connectionist models of developmental disorders to offer insights into the efficacy of interventions. Based on a range of computational simulation results, we assess factors that influence the effectiveness of interventions for reading, language, and other cognitive developmental disorders. The analysis provides a level of mechanistic detail that is generally lacking in behavioural approaches to intervention. We review an extended programme of modelling work in four sections. In the first, we consider long-term outcomes and the possibility of compensated outcomes and resolution of early delays. In the second section, we address methods to remediate atypical development in a single network. In the third section, we address interventions to encourage compensation via alternative pathways. In the final section, we consider the key issue of individual differences in response to intervention. Together with advances in understanding the neural basis of developmental disorders and neural responses to training, formal computational approaches can spur theoretical progress to narrow the gap between the theory and practice of intervention