Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Discussion <s> With increased public awareness of the early signs and recent American Academy of Pediatrics recommendations that all 18- and 24-month-olds be screened for autism spectrum disorders, there is an increasing need for diagnostic assessment of very young children. However, unique challenges exist in applying current diagnostic guidelines for autism spectrum disorders to children under the age of 2 years. In this article, we address challenges related to early detection, diagnosis, and treatment of autism spectrum disorders in this age group. We provide a comprehensive review of findings from recent studies on the early development of children with autism spectrum disorders, summarizing current knowledge on early signs of autism spectrum disorders, the screening properties of early detection tools, and current best practice for diagnostic assessment of autism spectrum disorders before 2 years of age. We also outline principles of effective intervention for children under the age of 2 with suspected/confirmed autism spectrum disorders. It is hoped that ongoing studies will provide an even stronger foundation for evidence-based diagnostic and intervention approaches for this critically important age group. Pediatrics 2009;123:1383‐1391 <s> BIB001 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Discussion <s> OBJECTIVE ::: To conduct a randomized, controlled trial to evaluate the efficacy of the Early Start Denver Model (ESDM), a comprehensive developmental behavioral intervention, for improving outcomes of toddlers diagnosed with autism spectrum disorder (ASD). ::: ::: ::: METHODS ::: Forty-eight children diagnosed with ASD between 18 and 30 months of age were randomly assigned to 1 of 2 groups: (1) ESDM intervention, which is based on developmental and applied behavioral analytic principles and delivered by trained therapists and parents for 2 years; or (2) referral to community providers for intervention commonly available in the community. ::: ::: ::: RESULTS ::: Compared with children who received community-intervention, children who received ESDM showed significant improvements in IQ, adaptive behavior, and autism diagnosis. Two years after entering intervention, the ESDM group on average improved 17.6 standard score points (1 SD: 15 points) compared with 7.0 points in the comparison group relative to baseline scores. The ESDM group maintained its rate of growth in adaptive behavior compared with a normative sample of typically developing children. In contrast, over the 2-year span, the comparison group showed greater delays in adaptive behavior. Children who received ESDM also were more likely to experience a change in diagnosis from autism to pervasive developmental disorder, not otherwise specified, than the comparison group. ::: ::: ::: CONCLUSIONS ::: This is the first randomized, controlled trial to demonstrate the efficacy of a comprehensive developmental behavioral intervention for toddlers with ASD for improving cognitive and adaptive behavior and reducing severity of ASD diagnosis. Results of this study underscore the importance of early detection of and intervention in autism. <s> BIB002 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Discussion <s> The developmental disabilities questions in the 2014 National Health Interview Survey (NHIS) were changed from previous years, including question reordering and a new approach to asking about autism spectrum disorder (ASD). This report examines survey-based estimates of the lifetime prevalence of ASD, intellectual disability (ID), and any other developmental delay (other DD) following the inclusion of a standalone ASD question, the inclusion of specific diagnoses in the ASD question, and the ASD question preceding the other DD question, and compares them with estimates from previous years.In NHIS, one child is randomly selected from each family to be the subject of detailed questions on health conditions, functional limitations, and health care utilization. Parents are asked if a doctor or health professional had ever told them that their child had each of a series of developmental disabilities. Prevalence estimates of ASD, ID, and other DD for children aged 3–17 years were calculated using data collected in 2011–2014.The estimated prevalence of ASD based on 2014 data was 2.24%, a significant increase from the estimated annualized prevalence of 1.25% based on 2011–2013 data. In contrast, the prevalence of other DD declined significantly from 4.84% based on 2011–2013 data to 3.57% based on 2014 data. The prevalence of ID did not significantly change from 2011–2013 (1.27%) to 2014 (1.10%). The prevalence of having any of the three conditions was constant across survey years.The revised question ordering and new approach to asking about developmental disabilities in the 2014 NHIS likely affected the prevalence estimates of these conditions. In previous years, it is likely that some parents of children diagnosed with ASD reported this developmental disability as other DD instead of, or in addition to, ASD. Following these changes, the 2014 ASD estimate was more similar to ASD prevalence estimates from other sources. <s> BIB003 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Discussion <s> Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes-about 65 genes out of an estimated several hundred-are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case-control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu. <s> BIB004 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Discussion <s> Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue to rise in prevalence, together affecting >10% of today’s pediatric population, the methods of diagnosis remain subjective, cumbersome and time intensive. With gaps upward of a year between initial suspicion and diagnosis, valuable time where treatments and behavioral interventions could be applied is lost as these disorders remain undetected. Methods to quickly and accurately assess risk for these, and other, developmental disorders are necessary to streamline the process of diagnosis and provide families access to much-needed therapies sooner. Using forward feature selection, as well as undersampling and 10-fold cross-validation, we trained and tested six machine learning models on complete 65-item Social Responsiveness Scale score sheets from 2925 individuals with either ASD (n=2775) or ADHD (n=150). We found that five of the 65 behaviors measured by this screening tool were sufficient to distinguish ASD from ADHD with high accuracy (area under the curve=0.965). These results support the hypotheses that (1) machine learning can be used to discern between autism and ADHD with high accuracy and (2) this distinction can be made using a small number of commonly measured behaviors. Our findings show promise for use as an electronically administered, caregiver-directed resource for preliminary risk evaluation and/or pre-clinical screening and triage that could help to speed the diagnosis of these disorders. <s> BIB005 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Discussion <s> Ample research has shown that intensive applied behavior analysis (ABA) treatment produces robust outcomes for individuals with autism spectrum disorder (ASD); however, little is known about the relationship between treatment intensity and treatment outcomes. The current study was designed to evaluate this relationship. Participants included 726 children, ages 1.5 to 12 years old, receiving community-based behavioral intervention services. Results indicated a strong relationship between treatment intensity and mastery of learning objectives, where higher treatment intensity predicted greater progress. Specifically, 35% of the variance in mastery of learning objectives was accounted for by treatment hours using standard linear regression, and 60% of variance was accounted for using artificial neural networks. These results add to the existing support for higher intensity treatment for children with ASD. <s> BIB006 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Discussion <s> Existing screening tools for early detection of autism are expensive, cumbersome, time-intensive, and sometimes fall short in predictive value. In this work, we apply Machine Learning (ML) to gold standard clinical data obtained across thousands of children at risk for autism spectrum disorders to create a low-cost, quick, and easy to apply autism screening tool that performs as well or better than most widely used standardized instruments. This new tool combines two screening methods into a single assessment, one based on short, structured parent-report questionnaires and the other on tagging key behaviors from short, semi-structured home videos of children. To overcome the scarcity, sparsity, and imbalance of training data, we apply creative feature selection, feature engineering, and novel feature encoding techniques. We allow for inconclusive determination where appropriate in order to boost screening accuracy when conclusive. We demonstrate a significant accuracy improvement over standard screening tools in a clinical study sample of 162 children. <s> BIB007
The purpose of this paper was to review literature that has applied supervised machine learning approaches to the study of ASD. A total of 45 articles that used supervised machine learning algorithms in ASD research and text mining initiatives were identified and reviewed. In the 35 reviewed ASD research studies, the most commonly used supervised machine learning algorithms were SVM and ADtree. Supervised machine learning algorithms were used in these studies to make binary predictions (often based on diagnoses) to aid ASD diagnosis and screening efforts, explore genetic underpinnings of ASD, and identify potential ASD biomarkers (e.g., using neuroimaging). In the 10 reviewed text mining articles, the most commonly used algorithms were naïve Bayes, SVM, and Random Forest. Supervised machine learning algorithms were used in the reviewed text mining articles to explore topics of interest in online ASD communities, to identify candidate ASD genes, and to investigate obscure links between ASD and other domains. The findings presented in this paper demonstrate that there is great value in and practical applications for the use of supervised machine learning in ASD research. Large and rich datasets, including genetic datasets, such as BIB004 , benefit greatly from the more flexible machine learning methods presented in this paper because these methods allow for multidimensional data to be fully utilized. Since the time from first concern to an ASD diagnosis is often over 2 years BIB001 ) and access to early intervention has been shown to improve outcomes BIB002 , the works reviewed including BIB005 and BIB007 present promising screening tools to help decrease the length of time and cost required for diagnosis. Unsupervised machine learning methods also provide practical, clinical classification tools that can be used in the field to classify or filter incoming data. For datasets that do not have attached classification labels, supervised learning is not useful. Since outside classification can take time, money, and effort, these data can be limited. However, huge amounts of labeled data are being created in the field every day and are well fitted to this kind of supervised learning. Another limitation of machine learning comes with the complexity of the models. While the more sophisticated machine learning models often outperform their simpler counterparts, there is a tradeoff between performance and understanding. For example, the neural network presented by BIB006 easily outperformed a simple linear regression; however, the internal parameters of neural networks cannot be easily interpreted. Researchers should consider if this understanding is more important than the results when selecting a machine learning model. Many of the above limitations and strengths apply to the field of Big Data and Machine Learning as a whole. Acquiring datasets large enough in terms of observations and explanatory variables to train complex models can be difficult, but with appropriately sized data, ML models often outperform simpler models. However, it is also important that these datasets are representative of the population of interest in order to avoid models that are systematically biased. While the predictive accuracy of ML models can be quite high, the models themselves are not always easily used for inferential conclusions which may decrease the adoption of such solutions. The added complexity of a ML model leads to an increase computation necessary to deploy in practical settings. This is where the science of Big Data and ML need to have the appropriate computational platform capable of handling both the data and the additional complexity of a ML model. Further research applying supervised machine learning methods to ASD research is needed. The reviewed literature has primarily applied machine learning to identify potential genetic markers and diagnosis; however, with estimates of 2% of the children diagnosed with ASD in the US BIB003 , in addition to expanding on the abovementioned areas of investigation, researchers should also investigate treatment methods, intensity, duration, and outcomes. Availability and affordability of data collecting devices have opened doors for the use of machine learning in ASD research. In the last decade, a growing number of supervised machine learning research studies have emerged in the field of ASD. Although limited, the existing literature described in the current review has demonstrated the potential of machine learning in ASD research. The capability of machine learning to extract knowledge from large collections of data is promising for future ASD research endeavors.