Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Behavioral and Neuroimaging Data <s> We apply artificial neural networks to the task of predicting the mastery of learning outcomes in response to behavioral therapy for children diagnosed with autism spectrum disorder. We report results for a sample size of 726 children, the largest sample size reported for a study of this nature to date. Our results show that neural networks substantially outperform the linear regression models reported in previous studies, and demonstrate the benefits of leveraging more sophisticated machine learning techniques in the autism research domain. <s> BIB001 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Behavioral and Neuroimaging Data <s> To facilitate precision medicine and whole-genome annotation, we developed a machine-learning technique that scores how strongly genetic variants affect RNA splicing, whose alteration contributes to many diseases. Analysis of more than 650,000 intronic and exonic variants revealed widespread patterns of mutation-driven aberrant splicing. Intronic disease mutations that are more than 30 nucleotides from any splice site alter splicing nine times as often as common variants, and missense exonic disease mutations that have the least impact on protein function are five times as likely as others to alter splicing. We detected tens of thousands of disease-causing mutations, including those involved in cancers and spinal muscular atrophy. Examination of intronic and exonic variants found using whole-genome sequencing of individuals with autism revealed misspliced genes with neurodevelopmental phenotypes. Our approach provides evidence for causal variants and should enable new discoveries in precision medicine. <s> BIB002 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Behavioral and Neuroimaging Data <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> BIB003 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Behavioral and Neuroimaging Data <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> BIB004 </s> Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review <s> Behavioral and Neuroimaging Data <s> Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrospectively analyzed diagnostic codes associated with de-identified electronic health records using a machine learning algorithm to characterize diagnostic patterns in decedents with ASD and matched decedent community controls. Participants were 91 decedents with ASD and 6,186 sex and birth year matched decedent community controls who had died since 1979, the majority of whom were middle aged or older adults at the time of their death. We analyzed all ICD-9 codes, V-codes, and E-codes available in the electronic health record and Elixhauser comorbidity categories associated with those codes. Diagnostic patterns distinguished decedents with ASD from decedent community controls with 75% sensitivity and 94% specificity solely based on their lifetime ICD-9 codes, V-codes, and E-codes. Decedents with ASD had higher rates of most conditions, including cardiovascular disease, motor problems, ear problems, urinary problems, digestive problems, side effects from long-term medication use, and nonspecific lab tests and encounters. In contrast, decedents with ASD had lower rates of cancer. Findings suggest distinctive lifetime diagnostic patterns among decedents with ASD and highlight the need for more research on health outcomes across the lifespan as the population of individuals with ASD ages. As a large wave of individuals with ASD diagnosed in the 1990s enters adulthood and middle age, knowledge about lifetime health problems will become increasingly important for care and prevention efforts. Autism Res 2018, 11: 1120-1128. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: This study looked at patterns of lifetime health problems to find differences between people with autism who had died and community controls who had died. People with autism had higher rates of most health problems, including cardiovascular, urinary, respiratory, digestive, and motor problems, in their electronic health records. They also had lower rates of cancer. More research is needed to understand these potential health risks as a large number of individuals with autism enter adulthood and middle age. <s> BIB005
Neural networks have also been leveraged by BIB001 BIB003 in a regression task to learn the relationship between treatment intensity and learning outcomes in the context of applied behavior analysis (ABA) treatment for ASD. The authors found that, compared to simple linear regression, neural networks were more accurately able to model the relationship between treatment intensity and learning outcomes, explaining substantially more variance for a sample size of 726 patients. This work leveraged only shallow neural networks with a single hidden layer and considered patients only within the early intervention age range. Despite these limitations, this work highlighted the advantage of neural networks to learn non-linear relationships without any prior knowledge of the functional form of those relationships. Genetic Data Neural networks have also been used to explore gene expression. BIB002 used an ensemble of neural networks to analyze DNA sequences and predict to what extent genetic variants may disrupt RNA splicing, an essential element of gene expression that may contribute to various disorders including ASD. The genomes of five individuals with ASD and 12 controls were scanned for single nucleotide variations (SNVs) resulting in approximately 42,000 SNVs per subject. Each neural network in the ensemble had 2 layers and used 41,820 potential input-to-hidden parameters and 960 hidden-to-output parameters. To overcome the numerical difficulties of fitting a single model, a Bayesian Markov chain Monte Carlo technique was deployed. Markov chain Monte Carlo (MCMC) sampling methods allow sampling of multiple distributions, which will scale to the dimensionality of the sample space  ). The final model was used to score genetic variations. Of the 171 genes found to be misregulated in individuals with ASD, 27% were found to have high expression in the brain compared to only 13% of the 249 genes identified in the controls. The authors highlighted 19 genes potentially linked to ASD with known neurological, neurobehavioral, or neurodevelopmental phenotypes. While some of these genes have been previously associated with ASD, others were new candidate ASD genes. RF classifiers have also been used to develop affordable and efficient ASD screening tools. BIB004 combined two independent classifiers to create an ASD screening tool based on a parental questionnaire and behaviors observed in home videos. ADI-R and ADOS scoresheets from children ages 18 to 84 months were cultivated from multiple repositories and used to train an RF for a parental questionnaire and an RF video classifier, respectively, with clinical diagnosis as the target variable. To help balance the data, ADI-R interviews were administered to a random sample of low-risk children. Since language development often affects the presentation of ASD, the classifiers were trained and tested on children older and younger than 4 years old separately. The parental classifier was trained on 2299 children with ASD, 100 children with TD, and 287 children with another condition. The video classifier was trained on 3310 children with ASD, 585 children with TD, and 364 children with another condition. Once trained, the video classifier used the presence and severity of behaviors, as determined by analysts watching two to three 1-min semi-structured home videos, as inputs. Final diagnosis utilized logistic regression on the results of the questionnaire and video classifiers and performed better than other established screening tools, including the Modified Checklist for Autism in Toddlers (MCHAT;  ) and the Child Behavior Checklist (CBCL;  , based on the ROC curves. Electronic Health Records BIB005 leveraged ICD-9 codes, V-codes, and E-codes from the electronic health records of 91 decedents with an ASD (or related) diagnosis and 6186 control decedents to build a random forest classifier. The goal of this study was not only to distinguish ASD from the control decedent but also to examine the lifetime health problems of those with ASD. From the first RF model, the top 50 ICD-9 codes, V-codes, and E-codes were chosen and were used to build a second, smaller random forest model. The model had an accuracy of 93%, sensitivity of 75%, specificity of 94%, and an AUC of 0.88. The 50 codes were then ranked in order of importance. The authors report that, overall, decedents with an ASD diagnosis have higher rates of nearly all 50 ICD-9 codes, V-codes, and E-codes.  